[Music] what do companies in e-commerce entertainment healthcare manufacturing marketing finance tech and hundreds of other industries all have in common you guessed it they all use data organizations of all kinds need data analysts to help them improve their processes identify opportunities and trends launch new products provide great customer service and make thoughtful decisions hi i'm tony a program manager at google and a data analyst myself i'd like to welcome you to the google data analytics certificate now there are lots of great reasons to earn this certificate maybe you're thinking about starting a career in the exciting world of data analytics or maybe you're just fascinated by the power of data as i am no matter what brought you here you're in the right place to kick-start a career and learn industry relevant skills in data analytics but first what exactly is data well i like to say that data is a collection of facts this collection can include numbers pictures videos words measurements observations and more once you have data analytics puts it to work through analysis data analysis is the collection transformation and organization of data in order to draw conclusions make predictions and drive informed decision making and it doesn't stop there data evolves over time which means this analysis or analytics as we call it can give us new information throughout data's entire life cycle data is everywhere you use and create data every day have you ever read reviews of a product before deciding whether or not to buy it that's data analysis or maybe you wear a fitness tracker to count your steps so you can stay active throughout the day that's data analysis but you don't just use data you also create huge amounts of it every single day anytime you use your phone look up something online stream music shop with a credit card post on social media or use gps to map a route you're creating data our digital world and the millions of smart devices inside of it have made the amount of data available truly mind-blowing here at google we process more than forty 000 searches every second that's 3.5 billion searches a day and 1.2 trillion searches every year here's another way to think about it youtube has almost 2 billion users if youtube users made up a country it would be the largest in the world all of that data is transforming the world around us the publication the economist recently called data the world's most valuable resource so it's easy to see why data analysts are so valued by their organizations and what exactly does a data analyst do put simply a data analyst is someone who collects transforms and organizes data in order to help make informed decisions besides the role itself one of the most exciting parts of being a data analyst is the number of opportunities available the demand for data analysts is greater than the number of qualified people to fill these job openings and this certificate program is a great first step in your journey to finding a job you love data analysts come from many different backgrounds and have all kinds of life experiences you don't need decades of work experience or an expensive education to get started many data analysts taught themselves the skills they needed to land their first job just like you're doing right now okay now let's talk more about what you're going to learn the google data analytics certificate is split into courses based on different processes for data analysis those are ask prepare process analyze share and act plan to watch these videos in order each one covers a new topic and every topic builds on what you've learned before making it easy to track your progress and you're in the driver's seat even though you might see things organized by weeks everything can be completed at your own pace so you decide how much you want to do each day by the end of the program you'll take everything you've learned and turn it into a project that you can use to show off your skills and while hiring managers at your job interviews now along the way you'll also hear from googlers that's what we call people who work here at google they'll give you an inside look at what it's like to work in our industry and share personal stories of how they got into the field they'll also give you some excellent tips on how to land your dream job stay tuned some of them are going to introduce themselves in just a sec so i'm angie i'm a program manager of engineering at google i truly believe that cleaning data is the heart and soul of data it's how you get to know your data it's quirks it's flaws it's mysteries i love a good mystery and it felt like a superpower almost like i was a detective and i'd gone in there and i'd really solved something hi i'm alex i'm a research scientist at google i research the different impacts of artificial intelligence on society and our users so my name is lyla jones and i am a part of our cloud team i get a chance to lead a team of amazing individuals that are focused on helping customers get to the cloud hi i'm evan i'm a learning portfolio manager here at google and i have one of the coolest jobs in the world where i get to look at all the different technologies that affect big data and then work them into training courses like this one for students to take i'll be your instructor for the first course i'll take you through each module that will cover a specific topic in a few different ways you'll have videos reading materials quizzes hands-on activities and discussion prompts for you to chat about with other students in an online forum i'm really excited to be guiding you through this course but i'm especially excited that you've chosen this adventure lifelong learning is something that i'm very passionate about growing up when i looked around i often didn't see many options available to me it wasn't until i started getting serious about my education that i realized i had the control to make my own opportunities with education being the key that would open those doors the more i learned and the harder i worked the more possibilities opened up had i not gone after that knowledge and continued challenging myself i may not be where i am today learning allowed me to grow personally be successful visit places i would never have seen and meet people i would never have known and now i'm going to introduce some of those great people hello i'm ximena financial analyst i'll be helping you learn how to ask the right questions about data the project you work on and the problems you're trying to solve hey my name is halle analytical lead i am so excited to show you how to prepare your data so it's ready for analysis hello i'm sally measurement and analytical lead together we'll cover how to process and clean your data cleaning data doesn't require soap and water i'm talking about making sure your data is complete correct and relevant to the problem you're trying to solve hey i'm ayanna global insights manager we'll be digging into analysis you'll learn how to collect transform and organize data so that you can use it to discover useful information draw conclusions and make great decisions my name is kevin and with my experience as director of analytics at google i'll guide you through what i think is the most exciting part of the data analysis process plan create and present effective and compelling data visualizations hello my name is carrie i can't wait to tell you about all the exciting things you can do with the programming language r are you ready hi i'm rishi global analytics skills curriculum manager i'm going to help you bring together everything you learn during this program by creating a case study that will dazzle any hiring manager just like the capstone of a great building shows everyone that it's complete your case study will signify your own great achievement of earning a google certificate in data analytics okay are you getting excited about the potential of becoming a data analyst so much as possible with data you're about to enter a whole new world ready let's go [Music] data data data i can't make bricks without clay any guesses who said this i'll give you a hint it wasn't a famous tech ceo or a data analyst the person who said this lived long before tech companies even existed but i bet you've still heard of him this line was said by sherlock holmes the famous detective created by sir arthur conan doyle what doyle meant was that holmes couldn't draw any conclusions which would be the bricks he mentioned without data or the clay you're probably not here to become a world famous detective but data is still the building block that you'll use for everything you do in your new data analyst career sherlock holmes would agree by starting this program you've shown that you and sherlock holmes have something in common you both have an interest in learning more that's one of the most important qualities that data analysts can have now there are a bunch of different ways to explore data but one of the great things about data analytics is that you can often learn how you want when you want that might mean doing your own research talking with people in the industry or taking online courses with that said welcome to your first course this is your introduction to the wonderful world of data analytics since data analytics is the science of data you use this course to begin to learn all about data data is basically a collection of facts or information and through analysis you'll learn how to use the data to draw conclusions and make predictions and decisions personally i didn't jump right into the data analytics field i thought data analysis was for computer engineers instead i started off with dreams of working in finance once i got through an internship though i realized it wasn't the career path i wanted to take i started to learn about financial planning and analysis and all of the work finance analysts were doing with data i realized that finance analysts are really just data analysts working in a finance department these analysts were helping to guide business decisions by knowing how to use data it was then i realized how powerful data is and i started to embrace it soon enough i realized i could do this data analysis myself data analytics is a big open world of opportunity there are so many areas your analysis skills can be applied and in all kinds of different ways if you're new to this world you'll learn how to identify which path in industry might suit your skills and your interests the best and for those of you who already have some experience we'll help you open doors to new and exciting opportunities one of the skills you'll gain from the program is how to follow the best practices that analysts use to help make data-driven decisions computers are one part of the process but analysts rely on so much more to make decisions that's why learning how to think analytically and using your other skills and traits on the job will make your work easier i know you already know how to make good decisions you chose to be here after all in this first course you'll learn more about each phase of the data analysis process ask prepare process analyze share and act as a data analyst you'll go through these steps as you use data to inform your decisions eventually you'll see how this program itself is in a way its own version of this process while i know you'll enjoy watching these videos your trip to the first course will include a whole lot more other videos will take the form of vignettes where you'll learn from data analytics professionals who are already established in their careers they'll offer words of wisdom as well as tales of their own experiences starting off on their career path you'll start your own data journal that will help you keep track of what you've learned throughout the course you'll also add your own thoughts about what you're learning as well throughout the program you'll read up on how to navigate this program in the world of data analytics you'll complete activities including some that will help you get in the mindset of a data analyst along the way you'll also have the chance to connect with your fellow learners discussion prompts will give you a chance to share your thoughts and at the same time see what your peers think about all that you're learning these prompts will help you build a community support system to use throughout the program alright enough talking let's get started on this exciting path your next step awaits welcome back at this point you've been introduced to the world of data analytics and what data analysts do you've also learned how this course will prepare you for a successful career as an analyst coming up you'll learn all the ways data can be used and you'll discover why data analysts are in such high demand i'm not exaggerating when i say every goal and success that my team and i have achieved couldn't have been done without data here at google all of our products are built on data and data driven decision making from concept to development to launch we're using data to figure out the best way forward and we're not alone countless other organizations also see the incredible value in data and of course the data analysts who help them make use of it so we know data opens up a lot of opportunities but to help you wrap your head around all the ways you can actually use data let's go over a few examples from everyday life you might not realize it but people analyze data all the time for instance i'm a morning person a long time ago i realized that i'm happier and more productive if i get to bed early and wake up early i came to this conclusion after noticing a pattern in my day-to-day experiences when i got seven hours of sleep and woke up at 6 30 i was the most successful so i thought about the relationship between this pattern and my daily life and i predicted that early to bed early to rise would be the right choice for me and i'm definitely my best self when i wake up bright and early i bet you've identified patterns and relationships in your life too maybe about your own sleep cycle or how you feel after eating certain foods or what time of day you like to work out all of these are great examples of real life patterns and relationships that you can use to make predictions about the right actions to take and that is a huge part of data analysis right there now let's put this process into a business setting you may remember from an earlier video that there's a ton of data out there and every minute of every hour of every day more data is being created businesses need a way to control all that data so they can use it to improve processes identify opportunities and trends launch new products serve customers and make thoughtful decisions for businesses to be on top of the competition they need to be on top of their data that's why these companies hire data analysts to control the waves of data they collect every day make sense of it and then draw conclusions or make predictions this is the process of turning data into insights and it's how analysts help businesses put all their data to good use this is actually a good way to think about analysis turning data into insights as a reminder the more detailed definition you learned earlier is that data analysis is the collection transformation and organization of data in order to draw conclusions make predictions and drive informed decision making so after analysts have created insights from data what happens well a lot those insights are shared with others decisions are made and businesses take action and here's where it can get really exciting data analytics can help organizations completely rethink something they do or point them in a totally new direction for example maybe data leads them to a new product or unique service or maybe it helps them find a new way to deliver an incredible customer experience it's these kinds of aha moments that can help businesses reach another level and that makes data analysts vital to any business now that you know more of the amazing ways data is being used every day you can see why data analysts are in such high demand we'll continue exploring how analysts can transform data into insights that lead to action and before you know it you'll be ready to help any organization find new and exciting ways to transform their data hi i'm cassie and i lead decision intelligence for google cloud decision intelligence is a combination of applied data science and the social and managerial sciences and it is all about harnessing the power and beauty of data i help google cloud and its customers turn their data into impact and make their businesses and the world better a data analyst is an explorer a detective and an artist all rolled into one analytics is the quest for inspiration you don't know what's going to inspire you before you explore before you take a look around and so when you begin you have no idea what you're going to find and whether you're even going to find anything you have to bravely dive into the unknown and discover what lies in your data there is a pervasive myth that someone who works in data should know the everything of data and i think that that's unhelpful because the universe of data has expanded and it's expanded so much that specialization becomes important it's very very difficult for one person to know and be the everything of data and so that's why we need these different roles and the advice that i give folks who are entering the space is to pick their specialization based on which flavor which type of impact best suits their personality now data science the discipline of making data useful is an umbrella term that encompasses three disciplines machine learning statistics and analytics these are separated by how many decisions you know you want to make before you begin with them so if you want to make a few important decisions under uncertainty that is statistics if you want to automate in other words make many many many decisions under uncertainty that is machine learning and ai but what if you don't know how many decisions you want to make before you begin what if what you're looking for is inspiration you want to encounter your unknown unknowns you want to understand your world that is analytics when you're considering data science and you're choosing which area to specialize in i recommend going with your personality which of the three excellences in data science feels like a better fit for you the excellence of statistics is rigor statisticians are essentially philosophers epistemologists so they are very very careful about protecting decision makers from coming to the wrong conclusion so if that care and rigor is what you are passionate about i would recommend statistics performance is the excellence of the machine learning and ai engineer you know that's the one for you if someone says to you i bet that you couldn't build an automation system that performs this task with 99.9999 accuracy and your response to that is watch me how about analytics the excellence of an analyst is speed how quickly can you surf through vast amounts of data to explore it and discover the gems the beautiful potential insights that are worth knowing about and bringing to your decision makers are you excited by the ambiguity of exploration are you excited by the idea of working on a lot of different things looking at a lot of different data sources and thinking through vast amounts of information while promising not to snooze past the important potential insights are you okay being told here is a whole lot of data no one has looked at it before go find something interesting and do you thrive on creative open-ended projects if that's you then analytics is probably the best fit for you a piece of advice that i have for analysts getting started on this journey is it can be pretty scary to explore the unknown but i suggest letting go a little bit of any temptations towards perfectionism and instead enjoying the fun the thrill of exploration don't worry about right answers see how quickly you can unwrap this gift and find out if there is anything fun in there it's like your birthday unwrapping a bunch of things some of them you like some of them you won't but isn't it fun to know what's actually in there hello again you've already learned about being a data analyst and how this program will help prepare you for your future career now it's time to explore the data ecosystem find out where data analytics fits into that system and go over some common misconceptions you might run into in the field of data analytics to put it simply an ecosystem is a group of elements that interact with one another ecosystems can be large like the jungle in a tropical rainforest or the australian outback or tiny like tadpoles in a puddle or bacteria on your skin and just like the kangaroos and koala bears and the australian outback data lives inside its own ecosystem too data ecosystems are made up of various elements that interact with one another in order to produce manage store organize analyze and share data these elements include hardware and software tools and the people who use them people like you data can also be found in something called the cloud the cloud is a place to keep data online rather than on a computer hard drive so instead of storing data somewhere inside your organization's network that data is accessed over the internet so the cloud is just a term we use to describe the virtual location the cloud plays a big part in the data ecosystem and as a data analyst it's your job to harness the power of that data ecosystem find the right information and provide the team with analysis that helps them make smart decisions for example you can tap into your retail storage database which is an ecosystem filled with customer names addresses previous purchase and customer reviews as a data analyst you could use this information to predict what these customers will buy in the future and make sure the store has the products in stock when they're needed as another example let's think about a data ecosystem used by human resources department this ecosystem would include information like postings from job websites stats on the current labor market employment rates and social media data on prospective employees a data analyst could use this information to help the team recruit new workers and improve employee engagement and retention rates but data ecosystems aren't just for stores and offices they work on farms too agricultural companies regularly use data ecosystems that include information including geological patterns and weather movements data analysts can use this data to help farmers predict crop yields some data analysts are even using data ecosystems to save real environmental ecosystems at the scripps institution of oceanography coral reefs all over the world are monitored digitally so they can see how organisms change over time track their growth and measure any increases or declines in individual colonies the possibilities are endless okay now let's talk about some common misconceptions you might come across first is the difference between data scientists and data analysts it's easy to confuse the two but what they do is actually very different data science is defined as creating new ways of modeling and understanding the unknown by using raw data here's a good way to think about it data scientists create new questions using data while analysts find answers to existing questions by creating insights from data sources there are also many words and phrases you'll hear throughout this course that are easy to get mixed up for example data analysis and data analytics sound the same but they're actually very different things let's start with analysis you've already learned that data analysis is the collection transformation and organization of data in order to draw conclusions make predictions and drive informed decision making data analytics in the simplest terms is the science of data it's a very broad concept that encompasses everything from the job of managing and using data to the tools and methods that data workers use each and every day so when you think about data data analysis and the data ecosystem it's important to understand that all of these things fit under the data analytics umbrella alright now that you know a little more about the data ecosystem and the differences between data analysis and data analytics you're ready to explore how data is used to make effective decisions you'll get to see data driven decision making in action so far you've discovered that there are many different ways data can be used in our everyday lives we use data when we wear a fitness tracker or read product reviews to make a purchase decision and in business we use data to learn more about our customers improve processes and help employees do their jobs more effectively but this is just the tip of the iceberg one of the most powerful ways you can put data to work is with data-driven decision-making data-driven decision-making is defined as using facts to guide business strategy organizations in many different industries are empowered to make better data-driven decisions by data analysts all the time the first step in data-driven decision making is figuring out the business need usually this is a problem that needs to be solved for example a problem could be a new company needing to establish better brand recognition so it can compete with bigger more well-known competitors or maybe an organization wants to improve a product and needs to figure out how to source parts from a more sustainable or ethically responsible supplier or it could be a business trying to solve the problem of unhappy employees low levels of engagement satisfaction and retention whatever the problem is once it's defined a data analyst finds data analyzes it and uses it to uncover trends patterns and relationships sometimes the data-driven strategy will build on what's worked in the past other times it can guide a business to branch out in a whole new direction let's look at a real world example think about a music or movie streaming service how do these companies know what people want to watch or listen to and how do they provide it well using data driven decision making they gather information about what their customers are currently listening to analyze it then use the insights they've gained to make suggestions for things people will most likely enjoy in the future this keeps customers happy and coming back for more which in turn means more revenue for the company another example of data-driven decision-making can be seen in the rise of e-commerce it wasn't long ago that most purchases were made in a physical store but the data showed people's preferences were changing so a lot of companies created entirely new business models that remove the physical store and let people shop right from their computers or mobile phones with products delivered right to their doorstep in fact data-driven decision making can be so powerful it can make entire business methods obsolete for example data help companies completely move away from corded phones and replace them with mobile phones by ensuring that data is built into every business strategy data analysts play a critical role in their company's success but it's important to note that no matter how valuable data driven decision making is data alone will never be as powerful as data combined with human experience observation and sometimes even intuition to get the most out of data driven decision making it's important to include insights from people who are familiar with the business problem these people are called subject matter experts and they have the ability to look at the results of data analysis and identify any inconsistencies make sense of gray areas and eventually validate choices being made organizations that work this way put data at the heart of every business strategy but also benefit from the insights of their people it's a win-win as a data analyst you play a key role in empowering these organizations to make data-driven decisions which is why it's so important for you to understand how data plays a part in the decision-making process we've covered a lot and i'm sure you have so much to think about already that's a good thing it means you've started collecting data and you're doing your own personal analysis that's what it's all about you've built a great base already as this course continues your knowledge and data analysis skills will continue to grow once you've established a solid foundation you'll apply what you've learned to the rest of the program the data analysis process will help provide a framework for everything you do soon you'll take your first graded assessment it's a great way to check your understanding of the concepts and build confidence in your knowledge everyone learns at different speeds so take your time get familiar with the concepts as soon as you feel ready you can go ahead and get started keep in mind if at any point you're not sure about a question you can always review the videos and readings to remind yourself of the answer we're all about open book tests here once you've passed you'll be all set to move on you've got this before you know it you'll be done with all of the courses and you'll be ready to create your own case study then if it's what you want to do you'll start your job search equipped with the tools and skills that will wow any company you talk to i can't wait to see where you go with data analytics for now though give yourself a pat on the back for a job well done see you soon welcome now that you have a solid foundation on the basics of data it's time to focus on some particular skills and characteristics that would be key to your future career as a data analyst we'll begin with five key skills move on to the characteristics of analytical thinking and then learn how data analysts balance their roles and responsibilities along the way you'll also discover how to tap into your own natural abilities for strategy technical expertise and data design these are incredibly helpful skills to have and you'll learn how to make them even stronger finally you'll be introduced to some fascinating real world examples of how data is influencing the lives of people all around the world alright let's get started earlier i told you that you already have analytical skills you just might not know it yet when learning new things sometimes people overlook their own skills but it's important you take the time to acknowledge them especially since these skills are going to help you as a data analyst in fact you're probably more prepared than you think don't believe me well let me prove it let's start by defining what i'm talking about here analytical skills are qualities and characteristics associated with solving problems using facts there are a lot of aspects to analytical skills but we'll focus on five essential points their curiosity understanding context having technical mindset data design and data strategy now you may be thinking i don't have these kinds of skills or i only have a couple of them but stay with me and i bet you'll change your mind let's start with curiosity curiosity is all about wanting to learn something curious people usually seek out new challenges and experiences this leads to knowledge the very fact that you're here with me right now demonstrates that you have curiosity all right that was an easy one now think about understanding context context is the condition in which something exists or happens this can be a structure or an environment a simple way of understanding context is by counting to five one two three four five all of those numbers exist in the context of one through five but what if a friend of yours said to you one two four five three well the three would be out of context simple right but it can be a little tricky there's a good chance that you might not even notice the three being out of context if you aren't paying close attention that's why listening and trying to understand the full picture is critical in your own life you put things into context all the time for example let's think about your grocery list if you group together items like flour sugar and yeast that's you adding context to your groceries this saves you time when you're at the baking out at the grocery store let's look at another example have you ever shuffled a deck of cards and noticed a joker if you're playing a game that doesn't include jokers identifying that card means you understand it's out of context remove it and you're much more likely to play a successful game alright so now we know you have both curiosity and the ability to understand context let's move on to the third skill a technical mindset a technical mindset involves the ability to break things down into smaller steps or pieces and work with them in an orderly and logical way for instance when paying your bills you probably already break down the process into smaller steps maybe you start by sorting them by the date they're due next you might add them up and compare that amount to the balance in your bank account this would help you see if you can pay your bills now or if you should wait until the next paycheck finally you'd pay them when you take something that seems like a single task like paying your bills and break it into smaller steps with an orderly process that's using a technical mindset now let's explore the fourth part of an analytical skill set data design data design is how you organize information as a data analyst design typically has to do with an actual database but again the same skills can easily be applied to everyday life for example think about the way you organize the context in your phone that's actually a type of data design maybe you list them by first name instead of last or maybe you use email addresses instead of their names what you're really doing is designing a clear logical list that lets you call or text the contact in a quick and simple way the last but definitely not least the fifth and final element of analytical skills is data strategy data strategy is the management of the people processes and tools used in data analysis let's break that down you manage people by making sure they know how to use the right data to find solutions to the problem you're working on for processes it's about making sure the path to that solution is clear and accessible and for tools you make sure the right technology is being used for the job now you may be doubting my ability to give you an example from real life that demonstrates data strategy but check this out imagine mowing a lawn step one would be reading the owner's manual for the mower that's making sure the people involved well you in this example know how to use the data available the manual would instruct you to put on protective eyewear and closed-toed shoes then it's on to step two making the process the path clear and accessible this would involve you walking around the lawn picking up large sticks or rocks that might get in your way finally for step three you check the lawnmower your tool to make sure it has enough gas and oil and is in working condition so the lawn can be moaned safely so there you have it now you know the five essential skills of a data analyst curiosity understanding context having a technical mindset data design and data strategy i told you that you are already an analytical thinker now you can start actively practicing these skills as you move through the rest of this course curious about what's next move on to the next video now that you know the five essential skills of a data analyst you're ready to learn more about what it means to think analytically people don't often think about thinking thinking is second nature to us it just happens automatically but there are actually many different ways to think some people think creatively some think critically and some people think in abstract ways let's talk about analytical thinking analytical thinking involves identifying and defining a problem and then solving it by using data in an organized step-by-step manner so as data analysts how do we think analytically well to answer that question we will now talk about a second set of five the five key aspects to analytical thinking they are visualization strategy problem orientation correlation and finally big picture and detail-oriented thinking let's start with visualization in data analytics visualization is the graphical representation of information some examples include graphs maps or other design elements visualization is important because visuals can help data analysts understand and explain information more effectively think about it like this if you are trying to explain the grand canyon to someone using words would be much more challenging than showing them a picture a visualization of the grand canyon would help you make your point much quicker now let's talk about the second part of analytical thinking being strategic with so much data available having a strategic mindset is key to staying focused and on track strategizing helps data analysts see what they want to achieve with the data and how they can get there strategy also helps improve the quality and usefulness of the data we collect by strategizing we know all our data is valuable and can help us accomplish our goals next up on the analytical thinking checklist being problem oriented data analysts use a problem-oriented approach in order to identify describe and solve problems it's all about keeping the problem top of mind throughout the entire project for example say a data analyst is told about the problem of a warehouse constantly running out of supplies they will move forward with different strategies and processes but the number one goal would always be solving the problem of keeping inventory on the shelves data analysts also ask a lot of questions this helps improve communication and saves time while working on a solution an example of that would be surveying customers about their experiences using a product and building insights from those questions to improve that product this leads us to the fourth quality of analytical thinking being able to identify a correlation between two or more pieces of data a correlation is like a relationship you can find all kinds of correlations in data maybe it's the relationship between the length of your hair and the amount of shampoo you need or maybe you notice a correlation between a rainier season leading to a high number of umbrellas being sold but as you start identifying correlations and data there's one thing you always want to keep in mind correlation does not equal causation in other words just because two pieces of data are both trending in the same direction that doesn't necessarily mean they are all related we'll learn more about that later and now the final piece of the analytical thinking puzzle big picture thinking this means being able to see the big picture as well as the details a jigsaw puzzle is a great way to think about this big picture thinking is like looking at a complete puzzle you can enjoy the whole picture without getting stuck on every tiny piece that went into making it if you only focus on individual pieces you wouldn't be able to see past that which is why big picture thinking is so important it helps you zoom out and see possibilities and opportunities this leads to exciting new ideas or innovations on the flip side detail-oriented thinking is all about figuring out all of the aspects that will help you execute a plan in other words the pieces that make up your puzzle there are all kinds of problems in the business world that can benefit from employees who have both a big picture and a detail-oriented way of thinking most of us are naturally better at one or the other but you can always develop the skills to fit both pieces together and now that you know the five aspects of analytical thinking visualization strategy problem orientation correlation and big picture and detail-oriented thinking you can put them to work for you when you're working with data and as you continue through this course you'll learn how let's recap what we've learned about analytical thinking so far the five key aspects are visualization strategy problem orientation correlation and using big picture and detail-oriented thinking and we've seen how you already use them in your everyday life we also talked about how different people naturally use certain types of thinking but that you can absolutely grow and develop the skills that might not come as easily to you this means you can become a versatile thinker which is a very important part of data analysis you might naturally be an analytical thinker but you can learn to think creatively and critically and be great at all three the more ways you can think the easier it is to think outside the box and come up with fresh ideas but why is it important to think in different ways well because in data analysis solutions are almost never right in front of you you need to think critically to find out the right questions to ask but you also need to think creatively to get new and unexpected answers let's talk about some of the questions data analysts ask when they're on the hunt for a solution here's one that will come up a lot what is the root cause of a problem a root cause is the reason why a problem occurs if we can identify and get rid of a root cross we can prevent that problem from happening again a simple way to wrap your head around root causes is with the process called the five wise in the five whys you ask why five times to reveal the root cause the fifth and final answer should give you some useful and sometimes surprising insights here's an example of the five why's in action let's say you wanted to make a blueberry pie but couldn't find any blueberries you'd be trying to solve a problem by asking why can't i make a blueberry pie the answer would be there are no blueberries at the store there's why number one so you then ask why were there no blueberries at the store and discover that the blueberry bushes don't have enough fruit this season that's why number two next you'd ask why was there not enough fruit this would lead to the fact that birds were eating all the berries why number three asked and answered now we get to why number four ask why a fourth time and the answer would be that although the birds normally prefer mulberries and don't eat blueberries the mulberry bushes didn't produce fruit this season so the birds are eating blueberries instead and finally we get to why number five which should reveal the root cause a late frost damaged the mulberry bushes so they didn't produce any fruit so you can't make a blueberry pie because of a late frost months ago see how the five wives can reveal some very surprising root causes this is a great trick to know and it can be a very helpful process in data analysis another question commonly asked by data analysts is where are the gaps in our process for this many people will use something called gap analysis gap analysis lets you examine and evaluate how a process works currently in order to get where you want to be in the future businesses conduct gap analysis to do all kinds of things such as improve a product or become more efficient the general approach to gap analysis is understanding where you are now compared to where you want to be then you can identify the gaps that exist between a current and future state and determine how to bridge them a third question that data analysts ask a lot is what did we not consider before this is a great way to think about what information or procedure might be missing from a process so you can identify ways to make better decisions and strategies moving forward these are just a few examples of the kinds of questions data analysts use at their jobs every day as you begin your career i'm sure you'll think of a whole lot more the way data analysts think and ask questions plays a big part in how businesses make decisions that's why analytical thinking and understanding how to ask the right questions can have such a huge impact on the overall success of a business later we'll talk more about how data-driven decisions can lead to successful outcomes in an earlier video you learned about five essential analytical skills as a reminder their curiosity understanding context having a technical mindset data design and data strategy in the next couple of videos we'll explore how these abilities all become part of data-driven decision making but first let's look at the concept of data-driven decision-making and why it's more likely to lead to successful outcomes you might remember that data-driven decision-making involves using facts to guide business strategy data analysts can tap into the power of data to do all kinds of amazing things with data they can gain valuable insights verify their theories or assumptions better understand opportunities and challenges support an objective help make a plan and much more in business data driven decision making can improve the results in a lot of different ways for example say a dairy farmer wants to start making and selling ice cream they could guess what flavors customers would like but there's a better way to get the information the farmer could survey people and ask them what flavors they prefer this gives the farmer the data they need to pick ice cream flavors people will enjoy here's another example let's say the president of an organization is curious about what perks employees value most she asked the human resources director who says people value casual dress code it's a gut feeling but the hr director backs it up with the fact that he sees a lot of people wearing jeans and t-shirts but what if this company were to use a more structured employee feedback process such as a survey it might reveal that employees actually enjoy free public transportation cards the most the human resources director just didn't realize that because he drives to work these are just some of the benefits of data-driven decision making it gives you greater confidence about your choice and your abilities to address business challenges it helps you become more proactive when an opportunity presents itself and it saves you time and effort when working towards a goal now let's learn more about how these five skills help you tap into all the potential of data-driven decision making first think about curiosity in context the more you learn about the power of data the more curious you're likely to become you'll start to see patterns and relationships in everyday life whether you're reading the news watching a movie or going to an appointment across town the analysts take their thinking in a step further by using context to make predictions research answers and eventually draw conclusions about what they've discovered this natural process is a great first step in becoming more data driven having a technical mindset comes next everyone has instincts or as in the case of our human resources director example gut feelings data analysts are no different they have gut feelings too but they've trained themselves to build on those feelings and use a more technical approach to explore them they do this by always seeking out the facts putting them to work through analysis and using the insights they gain to make informed decisions next we come to data design which has a strong connection to data-driven decision making to put it simply designing your data so that it's organized in a logical way makes it easy for data analysts to access understand and make the most of available information and it's important to keep in mind that data design doesn't just apply to databases this kind of thinking can work with all sorts of real life situations too the basic idea is this if you make decisions that are informed by data you are more likely to make more informed and effective decisions the final ability is data strategy which incorporates the people processes and tools used to solve a problem this is a big one to remember because data strategy gives you a high level view of the path you'll need to take to achieve your goals also data-driven decision making isn't a one-person job it's much more likely to be successful if everyone is on board and on the same page so it's important to make sure specific procedures are in place and that your technology being used is aligned with your data-driven strategy now you know how these five essential analytical skills work towards making better data-driven decisions so far many of the examples you've heard are hypothetical that means they could be true in theory but aren't specific real world cases next we'll look at some real examples i can't wait to share how data analysts put data to work for amazing results in this video i'm going to share some case studies that highlight the incredible work data analysts do each of these scenarios shows off the power of data-driven decision-making in unexpected ways the first story is about google as i mentioned a little while back here at google our mission is to organize the world's information and make it universally accessible and useful all of our products from idea to development to launch are built on data and data driven decision making there are tons of examples here at google of people using facts to create business strategy but one of the most famous ones has to do with google's human resources so here's how it went the hr department wanted to know if there was value in having managers were their contributions worthwhile or should everyone just be an individual contributor to answer that question google's people analytics team look at past performance reviews and employee surveys the data they found was plotted on a graph because as you've learned visuals are extremely helpful when trying to understand a problem or concept the graph revealed that googlers had positive feelings about their managers but the data was pretty general and the team wanted to learn more so they dug deeper and split the data into quartiles a quartile divides data points into four equal parts or quarters here's where the really cool stuff started happening the data analyst discovered that there was a big difference between the very top and the very bottom quartiles as it turned out the teams with the best managers were significantly happier more productive and more likely to want to keep working at google this confirmed that managers were valued and make a big difference therefore the idea of having only individual contributors was not implemented but there was still more work to do just knowing that great managers create great results doesn't lead to actionable insights you have to identify what exactly makes a great manager so the team took two additional steps to collect more data first they launched an awards program where employees can nominate their favorite managers for every submission you had to provide examples or data about what makes that manager great the second step involved interviewing managers who are graft on the top and bottom quartiles this helped the analytics team see the differences between successful and less successful management behaviors the best behaviors were identified as were the most common reasons for a manager needing improvement the final step was sharing these insights and putting a procedure in place for evaluating managers with these qualities in mind this data-driven decision continues to create an exceptional company culture for my colleagues and me thanks data another interesting example comes from the nonprofit sector nonprofits are organizations dedicated to advancing a social cause or advocating for a particular effort such as food security education or the arts in this case data analysts research how journalists can make a more meaningful impact for the non-profits they would write about because journalists write for newspapers magazines and other news outlets they can help non-profits reach readers like you and me who then take action to help non-profits reach their goals for instance say you read about the problem of climate change in an online magazine if the article is effective you learn more about the cause and might even be compelled to make greener choices in your day-to-day life volunteer for a non-profit or make a donation that's an example of the journalists work bring about awareness understanding and engagement so back to the story the data analysts use the tracker to monitor story topics clicks web traffic comments shares and more then they evaluated the information to make recommendations for how the journalists could do their jobs even better in the end they came up with some great ideas for how nonprofits and journalists can motivate people everywhere to work together and make the world a better place there's really no end to what you can do as a data analyst as you progress through this program you'll discover even more possibilities great job following along with the topics in these past few videos you learned all about analytical skills and the five key characteristics of data analysts you probably even learned that you're a pro at most of these already next you discovered what it means to think analytically and the specific skills data analysts develop to help them do it you explore tools and processes that enable data analysts to pinpoint a problem and ask the right questions in order to solve it finally some real world stories helped illustrate why data-driven decision-making is usually more successful than other methods you're building a wonderful foundation for your career as a data analyst with every video your skills will continue to expand and your understanding of key data analytics concepts will only get stronger soon you'll have a chance to test out everything you've learned this is a really useful opportunity to check your understanding of all the concepts we've discussed and if you're ever unsure about a question you can review the videos and readings to find the answer this is another awesome way to practice collecting data keep up the great work hey it's great to have you back so we've talked a little bit about the data analysis process as a quick refresher the data analysis process phases are ask prepare process analyze share and act you might remember me saying earlier that this entire program is modeled after these steps so now we're going to really dig in and explore how each of these phases work together but i'm getting a little ahead of myself first let's spend a little time understanding the data life cycle no data isn't actually alive but it does have a life cycle so how do data analysts bring data to life well it starts with the right data analysis tool these include spreadsheets databases query languages and visualization software don't worry if you don't know how these work or even what they are at one point every data analyst has been right where you are right now and they probably had a lot of the same questions i remember when i first started learning about spreadsheets i was a young intern and the company i was working for was in the middle of a big systems change that meant we had to move tons of reports from the old system to the new one after a few weeks i noticed that even the people who are further in their careers were not as technically minded as i was so that became a great opportunity for me to add value my aha spreadsheet moment came when i started researching shortcuts that i could use to work with the spreadsheets more efficiently this would really streamline the process of getting those reports moved over to the new system once everything started flowing i remember getting emails from other finance analysts at the company they were so grateful that someone had come in and fixed a problem that no one else could that inspired me to go even further and learn how to use spreadsheets in all sorts of incredible ways as you continue through this course i bet you'll be just as impressed as i was and before you know it you'll bring data to life too let's get started here's a question for you when you think about a life cycle what's the first thing that comes to mind now i'm not a mind reader but i know whatever you're thinking is right there's actually no wrong answer because everything has a life cycle one of the most well known examples of a life cycle is a butterfly butterflies begin as eggs hatch into caterpillars and then become a chrysalis that's where the real magic happens data has a life cycle of its own too in this video we're going to talk about each of the stages in that life cycle to help you understand the individual phases data goes through the life cycle of data is plan capture manage analyze archive and destroy let's start with the first phase planning this actually happens well before starting an analysis project during planning a business decides what kind of data it needs how it will be managed throughout its life cycle who will be responsible for it and the optimal outcomes for example let's say an electricity provider wanted to gain insights into how to save people energy in the planning phase they might decide to capture information on how much electricity its customers use each year what types of buildings are being powered and what types of devices are being powered inside of them the electricity company would also decide which team members will be responsible for collecting storing and sharing that data all of this happens during planning and it helps set up the rest of the project the next phase is when you capture data this is where data is collected from a variety of different sources and brought into the organization with so much data being created every day the ways to collect it are truly endless one common method is getting data from outside resources for example if you're doing data analysis on weather patterns you'd probably get data from a publicly available data set like the national climatic data center another way to get data is from a company's own documents and files which are usually stored inside a database while we've mentioned databases before we haven't gone into too much detail about what they are a database is a collection of data stored in a computer system in the case of our electricity provider the business would probably measure data usage among its customers within a database that it owns as a quick note when you maintain a database of customer information ensuring data integrity credibility and privacy are all important concerns you'll learn a lot more about that later on now that we've captured our data we move on to the next phase of the data life cycle manage here we're talking about how we care for our data how and where it's stored the tools used to keep it safe and secure and the actions taken to make sure that it's maintained properly this phase is very important to data cleansing which we'll cover later on next it's time to analyze your data this is where data analysts really shine in this phase the data is used to solve problems make great decisions and support business goals for example one of our electricity companies goals might be to find ways to help customers save energy moving on the data lifecycle now evolves to the archive phase archiving means storing data in a place where it's still available but may not be used again during analysis analysts handle huge amounts of data can you imagine if we had to sort through all of the available data that's out there even if it was no longer useful and relevant to our work it makes way more sense to archive it than to keep it around and finally the last step of the data life cycle the destroy phase yes it sounds sad but when you destroy data it won't hurt a bit so let's get back to our electricity provider example they would have data stored on multiple hard drives to destroy it the company would use a secure data erasure software if there were any paper files they would be shredded too this is important for protecting a company's private information as well as private data about its customers and there you have it the data life cycle and now that you understand the different phases data goes through during its life cycle you can better understand how to approach the data analysis process which we'll talk about soon now that you understand all the phases of the data life cycle it's time to move on to the phases of data analysis they sound similar but are two different things data analysis isn't a life cycle it's the process of analyzing data coming up we'll look at each step of the data analysis process and how it will relate to your work as a data analyst even this program is designed to follow these steps understanding these connections will help guide your own analysis and your work in this program you've already learned that this program is modeled after the stages of the data analysis process this program is split into courses six of which are based upon the steps of data analysis ask prepare process analyze share and act okay let's start with the first step in data analysis the ass phase in this phase we do two things we define the problem to be solved and we make sure that we fully understand stakeholder expectations stakeholders hold a stake in the project they are people who have invested time and resources into our project and are interested in the outcome let's break that down first defining a problem means you look at the current state and identify how it's different from the ideal state usually there's an obstacle we need to get rid of or something wrong that needs to be fixed for instance a sports arena might want to reduce the time fans spend waiting in the ticket line the obstacle is figuring out how to get the customers to their seats more quickly another important part of the ass phase is understanding stakeholder expectations the first step here is to determine who the stakeholders are that may include your manager an executive sponsor or your sales partners there can be lots of stakeholders but what they all have in common is that they help make decisions influence actions and strategies and have specific goals they want to meet they also care about the project and that's why it's so important to understand their expectations for instance if your manager assigns you a data analysis project related to business risk it would be smart to confirm whether they want to include all types of risks that could affect the company or just risk related to weather such as hurricanes and tornadoes communicating with your stakeholders is key in making sure you stay engaged and on track throughout the project so as a data analyst developing strong communication strategies is very important this part of the ask phase helps you keep focused on the problem itself not just its symptoms as you learned earlier the five why's are extremely helpful here in an upcoming course you'll learn how to ask effective questions and define the problem by working with stakeholders you'll also cover strategies that can help you share what you discover in a way that keeps people interested after that we'll move on to the prepare step of the data analysis process this is where data analysts collect and store data they'll use for the upcoming analysis process you'll learn more about the different types of data and how to identify which kinds of data are most useful for solving a particular problem you'll also discover why it's so important that your data and results are objective and unbiased in other words any decisions made from your analysis should always be based on facts and be fair and impartial next is the process step here data analysts find and eliminate any errors and inaccuracies that can get in the way of results this usually means cleaning data transforming it into more useful format combining two or more data sets to make information more complete and removing outliers which are any data points that could skew the information after that you'll learn how to check the data you prepared to make sure it's complete and correct this phase is all about getting the details right so you'll also fix typos inconsistencies or missing and inaccurate data and to top it off you'll gain strategies for verifying and sharing your data cleansing with stakeholders then it's time to analyze analyzing the data you've collected involves using tools to transform and organize that information so that you can draw useful conclusions make predictions and drive informed decision making there are lots of powerful tools data analysts use in their work and in this course you'll learn about two of them spreadsheets and structure query language or sql which is often pronounced sql the next course is based on the share phase here you'll learn how data analysts interpret results and share them with others to help stakeholders make effective data-driven decisions in the share phase visualization is a data analyst's best friend so this course will highlight why visualization is essential to getting others to understand what your data is telling you with the right visuals facts and figures become so much easier to see and complex concepts become easier to understand we'll explore different kinds of visuals and some great data visualization tools you'll also practice your own presentation skills by creating compelling slide shows and learning how to be fully prepared to answer questions then we'll take a break from the data analysis process to show you all of the really cool things you can do with the programming language r you don't need to be familiar with r or programming languages in general just know that r is a popular tool for data manipulation calculation and visualization and for our final data analysis phase we have act this is the exciting moment when the business takes all of the insights you the data analysts have provided and puts them to work in order to solve the original business problem and will be acting on what you've learned throughout this program this is when you'll prepare for your job search and have the chance to complete a case study project it's a great opportunity for you to bring together everything you've worked on throughout this course plus adding a case study to your portfolio helps you stand out from the other candidates when you interview for your first data analyst job now you know the different steps of the data analysis process and how our course reflects it you have everything you need to understand how this course works and my fellow googlers and i will be here to guide you every step of the way regardless of what type of data analysis you're conducting the process is generally the same the example that i'll walk through is that of our employee engagement survey but you can imagine that this process applies to just about any data analysis that you're going to conduct as an analyst the first thing you want to do is ask you want to ask all of the right questions at the beginning of the engagement so you better understand what your leaders and stakeholders need from this analysis so the types of questions that i generally ask are around you know what is the problem that we're trying to solve what is the purpose of this analysis what are we hoping to learn from it so after you've asked all the right questions and you've wrapped your arms around the scope of the analysis you need to conduct the next step is to prepare we need to be thinking about what type of data we need to answer those key questions this could be anything from quantitative data or qualitative data it could be cross-sectional or point in time versus longitudinal over a long period of time we need to be thinking about the type of data we need in order to answer the questions that we've set out to answer based on what we learned when we asked the right questions we also need to be thinking about how we're going to collect that data or if we need to collect that data it may be the case that we need to collect this data brand new and so we need to think about what type of data we're going to be collecting and how for our employee engagement survey we do that via a survey of both quantitative and qualitative questions but it may actually be the case that for many analyses the data that you're looking for already exists then it's a question of working with those data owners to make sure that you're able to leverage that data and use it responsibly after you've done all the hard work to collect your data now you need to process that data it begins with cleaning this to me is the most fun part of the data analytics process you know we can think of it as the initial introduction or the handshake you know hello to your data this is where you get a chance to understand its structure its quirks its nuances and you really get a chance to understand deeply what type of data you're going to be working with and understanding what potential that data has to answer all of your questions this is such an important part too where we're running through all of our quality assurance checks for example do we have all of the data that we anticipated we would have are we missing data at random or is it missing in a systematic way such that maybe something went wrong with our data collection effort if needed did we code all of our data the right way are there any outliers that we need to treat differently you know this is the part where you spend a lot of time really digging deeply into the structure and nuance of the data to make sure that you're able to analyze it appropriately and responsibly after cleaning our data and running all of our quality assurance checks now is the point where we analyze our data making sure to do so in as objective and unbiased a way as possible to do this the first thing we do is run through a series of analyses that we've already planned ahead of time based on the questions that we know we want to answer from the very very beginning of the process one thing that's probably the hardest about this particular process the hardest thing about analyzing data is that we as analysts are are trained to look for patterns and over time as we become better and better at our jobs what we'll often find is that we can start to intuit what we might see in the data we might have a sneaking suspicion as to what the data are going to tell us and this is the point where we have to take a step back and let the data speak for itself you know as data analysts we are storytellers but we also have to keep in mind that it is not our story to tell that story belongs to the data and it is our job as analysts to amplify and tell that story in as unbiased and objective a way as possible the next step is to share all of the data and insights that you've generated from your analyses now typically for our employee engagement survey we start by sharing the high level findings with our executive team we want them to have a landscape view of how the organization is feeling and we want to make sure that there aren't any surprises as they dig deeper and deeper into the data to understand how teams are feeling and how individual employees are feeling all of this work from asking the right questions to collecting your data to analyzing and sharing doesn't mean much of anything if we aren't taking action on what we've just learned this to me is the most critical part especially of our employee engagement survey i like to say that the survey is actually the easy part and acting on the results is really where the real work begins this is where we use all of those data driven insights to decide what types of interventions we want to introduce not only the organizational level but also at the team level as well so we might find for example that the organization is working on a series of of interventions to help improve part of the employee experience whereas individual teams have additional roles responsibilities to play to either bolster some of those efforts or to introduce new ones to sort of better meet their team where where their strengths and opportunity areas are the data analysis process is rigorous but it is lengthy and i can completely appreciate that we as data analysts get so excited about just diving right into the data and doing what we do best the challenge is that if we don't work through the process and in its entirety if we try to skip steps we're not going to be able to elicit the the insights that we're looking for i absolutely love my job i i such a deep appreciation for for data and what it can do and and what type of insight we can derive from it i'm looking forward to introducing you to some of the tools data analysts use each and every day there are tons of options out there but the most common ones you'll see analysts use are spreadsheets query languages and visualization tools and this video is going to give you a quick look at how these tools are being used by data analysts every day believe it or not i was several years into my accounting and finance career before i saw all of these tools working together at that point i was very experienced with spreadsheets and had worked in large data sets with some of the traditional database programs i had the foundational skill set to use query languages and i had dabbled in visualizations but i had never brought them all together then i got hired here at google and it was so eye-opening to come into a place like this with an abundance of information everywhere you look as an analyst at google the true power of these tools became so much clearer to me i became more focused on really maximizing everything these tools could do streamlining my reporting and just making my work simpler all of a sudden i had a lot more time and space to dedicate to identifying new problems to solve and driving decision making without a doubt once you've learned the power of these tools you will be well on your way to becoming the best data analyst you can possibly be alright i hope that story has you even more motivated for this course let's get started with spreadsheets again there are lots of different spreadsheet solutions but two popular options are microsoft excel and google sheets to put it simply a spreadsheet is a digital worksheet it stores organizes and sorts data this is important because the usefulness of your data depends on how well it's structured when you put your data into a spreadsheet you can see patterns group informations and easily find the information you need spreadsheets also have some really useful features called formulas and functions a formula is a set of instructions that performs a specific calculation using the data in a spreadsheet formulas can do basic things like add subtract multiply and divide but they don't stop there you can also use formulas to find the average of a number set look up a particular value return the sum of a set of values that meets a particular rule and so much more a function is a preset command that automatically performs a specific process or task using the data in the spreadsheet that sounds pretty technical i know so let's break it down just think of a function as a simpler more efficient way of doing something that would normally take a lot of time in other words functions can help make you more efficient those are the spreadsheet basics for now later on you'll see them in action and start working with spreadsheets yourself the next data analysis tool is called query language a query language is a computer programming language that allows you to retrieve and manipulate data from a database you'll learn something called structured query language more commonly known as sql sql is a language that lets data analysts communicate with a database a database is a collection of data stored in a computer system sql is the most widely used structured query language for a couple of reasons it's easy to understand and works very well with all kinds of databases with sql data analysts can access the data they need by making a query although query means question i like to think of it as more of a request so you're requesting that the database do something for you you can ask it to do a lot of different things such as insert delete select or update data okay that's a top-level look at sql in a later video we'll explore it further and use sql to do some really cool things with data lastly let's talk about data visualization you've learned that data visualization is the graphical representation of information some examples include graphs maps and tables most people process visuals more easily than words alone that's why visualizations are so important they help data analysts communicate their insights to others in an effective and compelling way when you think about the data analysis process after data is prepared processed and analyzed the insights are visualized so it can be understood and shared this makes it easier for stakeholders to draw conclusions make decisions and come up with strategies some popular visualization tools are tableau and looker data analysts like using tableau because it helps them create visuals that are very easy to understand this means that even non-technical users can get the information they need looker is also popular with data analysts because it gives them an easy way to create visuals based on the results of a query with looker you can give stakeholders a complete picture of your work by showing them visualization data and the actual data related to it all visualization tools have great features that are useful in different situations soon you'll learn how to decide which tool to use for a particular job and that's everything you need to know about the data life cycle and the data analysis process you'll get a chance to test out what you know so you can feel confident moving forward in this course feel free to take some time to refamiliarize yourself with the concepts and when you're ready give it your best shot if you're ever unsure of an answer you can always go back and review the videos and readings then you'll be ready to move on to the next set of videos where we'll continue exploring the data analytics tools you've already covered and you'll get some really fascinating insights into exactly how they work before long you'll have the knowledge and confidence to start using them yourself stay tuned welcome back in the next few videos you'll continue to explore the data analytics tools we discussed earlier and you'll get the chance to see them in action a little bit this will give you a clearer picture of how to use these tools the rest of the program will build on from what you learn here we'll start with a closer look at spreadsheets we'll break spreadsheets down to their basics to better understand a few of their features and functions you'll also learn how you might want to use them in your work as a data analyst for example how do you sort your data to make it easier to use we'll find out next we'll see sql in action data analysts use sql in their work all the time like when they need a large amount of data in seconds to help answer a quick business question chances are you're not familiar with sql that's okay you'll learn how using sql is just like ordering food at a super speedy restaurant your sql query might not be as delicious but you won't have to wait long to get your order speaking of food what better topic than dessert you can think of data visualization as the dessert to the meal of data analytics it's served at the end of your analysis after you've done what you need to get the right data for a question or task we've already seen that visualizations come in a lot of forms like graphs or charts and just like dessert they're a treat to look at you'll learn more about these visual representations and see other examples of how they might look then you'll get to talk about visualizations with other future data analysts just like yourself we'll wrap things up with an assessment but you'll have time to review what you've learned before then okay let's keep going by the way is anyone else hungry now on october 17 2019 we celebrated the 40th anniversary of a very special event well special for people like me anyway in 1979 visit calc was introduced to the world as the first computer spreadsheet program while spreadsheets have changed a lot since then it was still an important achievement and so now we celebrate october 17th every year as spreadsheet day while there's a good chance you've never been to a spreadsheet day party spreadsheets are a big part of data analytics the sooner you make friends with spreadsheets the better trust me they'll save you a lot of time as a data analyst and make your job easier this spreadsheet is one example of how an organized spreadsheet might look in this video we'll demonstrate some basic spreadsheet concepts for all of you who are new to this world this might be a review for some of you more experienced folks out there but it never hurts to practice what you know plus you might still learn a new trick or two i showed you this image earlier let's explore it further because it's a great example of the three main features of a spreadsheet cells rows and columns they'll be a part of almost everything you do in a spreadsheet from making a simple grocery list to analyzing a complex data set i use spreadsheets to manage everything from my own personal finances to the annual homecoming party my friends and i have every year i'm the planner so i use a spreadsheet to keep things in order making sure we have everything we need speaking of keeping things in order the columns in a spreadsheet are ordered by letter and the rows are ordered by number so when you talk about a specific cell you name it by combining the column letter and row number where the cell is located for example in this spreadsheet the word row is in cell d3 pretty simple right let's get started in an actual spreadsheet you can complete all of these steps in just about any spreadsheet program let's get to know your spreadsheet a little better now alright we'll start with some basic operations keep in mind as an analyst you won't always create your own data sets but for now let's do just that i'll click in cell a2 and type my first name in the cell like this next i'll click in cell b2 and type my last name don't worry if your name doesn't fit in the cell you can always make the columns wider if you need to all you have to do is click and drag the right edge of the column until the name fits or you can also use the text wrapping feature which will set cells to automatically change their height and allow the text in the cell to fit to use this feature select the cells columns or rows with text then use the format menu to look at the text wrapping options it is automatically set to allow the text to overflow out of the cell but you can wrap the text instead so all of the text is visible the clip option will cut off the text in the cell so only the text that fits is visible there it is we've added data now let's label the data this is important for organization adding labels to the top of the columns will make it easier to reference and find data later on when you're doing analysis the column labels are usually called attributes an attribute is a characteristic or quality of data used to label a column in a table you might hear them called variables or a few other names too all right let's add some attributes to our data i'll click in cell a1 and type the words first name in cell b1 i'll type last name we'll make these attributes bold so they stand out more spreadsheets can be really big so you want to make sure your data is clearly labeled and easy to find so let's make these attributes stand out i can use my cursor to select the cells with the attributes then i'll click the bold icon to make the attributes bold looking good so far ready to add some more data let's start with some new attributes first i'll add a column for age by typing age in cell c1 then i'll add two more attributes in the next two columns let's go with favorite color and favorite dessert i'll make them bold too and to fit the labels in the cells i'll adjust the size of the columns just like before now keep in mind there are more ways to adjust the size of columns and rows if you have questions about using spreadsheets a quick search online will usually help you find what you need we've also included a reading with more tips and information about spreadsheets okay let's get back to it now i can add my own data to the data set i'll type in my age and favorite color and dessert in the appropriate cells next i'll add data for two more people we now have three rows of data in a data set a row is called an observation an observation includes all of the attributes for something contained in a row of a data table in this case row 3 is an observation of willa stein because we see all of her attributes in this row so now we know spreadsheets let you do lots of things with data you can store and organize data like we've done in this spreadsheet but you can go even further and reorganize existing data too here i'll show you how let's say we want to organize our data by age there's a simple way to do that first we'll need to select all of our columns with data so that all of it gets reorganized together then we can go to our data menu here we have some options let's select sort range this will let us choose how to organize the column next we'll choose a to z which will organize our numbers in order from the smallest to the largest now we want to watch out for our header row which is the word age the attribute for this column we'll check that box this makes sure that the word age stays in place all right now we're ready to sort voila we just reorganized our data by sorting it from the smallest number to largest and as we go further you'll discover lots of other ways to work with data in a spreadsheet including functions and formulas let's finish with a quick example of a formula you can think of formulas as one way of manipulating data in a spreadsheet formulas are like a calculator but more powerful a formula is a set of instructions that performs a specific calculation using the data in a spreadsheet to do this the formula uses cell references for the values it's calculating let me show you here we go we'll click in the next cell in the age column then we'll type an equal sign all formulas begin with this symbol next we type average this is the function we are using in the formula we've briefly discussed how functions work before but it's okay if you don't completely understand them yet we'll take a closer look later on in this case we follow the function average with the left parentheses now we can add the names of the cells where we find the data we're using these are the cell references the formula will use to make its calculation we'll start at the top with cell c2 c2 represents the value in the cell in this case 36 then we'll add a colon next to it which shows that we have a range of numbers in consecutive cells finally we complete our formula by adding the last cell reference in the range c4 and a right parenthesis to end it then we press enter to perform the calculation and there it is the formula has given us the average age of the ages in this data set we've just analyzed some data we'll want to store the data for later use in google sheets a spreadsheet is automatically saved in your google drive for excel and other spreadsheets you'll save them as a file and now you know some basics for using spreadsheets once you're used to these concepts you'll be able to learn even more about spreadsheet tools it's a lot to digest so feel free to re-watch and practice on your own you can even make your own version of this spreadsheet with your own data we'll work together in a spreadsheet soon as well for now good job for sticking with me through this it'll be worth it as you might remember earlier we touched on the query language sql in this video you'll see sql in action and finally learn what you can do with it by taking a look at some examples of specific queries i guess you can call this the sql sql we'll try to make this one at least as good as the first remember sql can do lots of the same things with data that spreadsheets can you can use it to store organize and analyze your data among other things but like any good sql it is on a larger scale bigger more action-packed think of it as super-sized spreadsheets for example you might want to consider a spreadsheet when you have a smaller data set like 100 rows but if your data seems to go on forever and your spreadsheet is struggling to keep up sql would be the way to go when you use sql you'll need a place where the sql language is understood if you've ever gone somewhere and not known the language it can be challenging to communicate you might think you're asking for one thing and get something completely different well sql knows that feeling sql needs a database that will understand its language so let's talk there are a number of databases out there that use sql you may use several of them during your time as a data analyst but here's the thing no matter which database you use sql basically works the same in each for example in sql queries are universal we've talked about queries before but it never hurts to have a refresher a query is the way we use sql to communicate with the database here's the structure of a basic query you can see that with this query we can select specific data from a table by adding where we can filter the data based on certain conditions all right let's get started we'll open our database and see how sql can communicate with it to do some simple data tasks first let's select our data we'll use an asterisk to select all of the data from the table and with that simple query the database calls up the table we need magic let's add where to the query to show how that changes with data we get you can see that the data now only shows movies that are in the action genre and that's it a basic query in sql pretty cool huh there are plenty of other commands that you'll use in queries as you continue for now though we can celebrate learning about three big ones select from and where as you continue the program you'll have the opportunity to use sql yourself so i hope that this video was a useful sneak peek at what's coming later like with any new language learning it takes time and now it's time to move on i'm angie i'm a program manager of engineering at google i'm currently working on the data analytics certificate and previously i was a researcher in people analytics i was also what i call an analytical mercenary working for a lot of different companies to help them make sense of their data every time i learn a new skill i feel like i'm learning how to speak all over again i remember the first time i learned sql i was so frustrated because everyone around me just it felt like they were fluent they knew exactly what they were doing and i remember struggling with the most basic things just like getting the data out of the table right or i remember somebody asked me just to find like an average of something and i kept on getting an error and it really does feel like you're learning a new language and you're at toddler level and everyone around you is like maybe fluent so my parents immigrated to this country when they were in their 30s so after you know they had learned another language and they had to start over and learn you know english and i remember as a child watching them struggle every day to pick up a new language to do really basic things like ask for help at the grocery store i remember calling the cable company when i was six asking them questions about the bill because my parents couldn't and i remember how hard they worked to learn this new language and to become fluent you know and every time i'm learning a new data language like sql or r i think about how hard that must have been and i i think if they can do that i can learn sql um if they can ask for help for the most basic of things i can ask the data analyst next to me you know how to write a sql statement how to get data out of a table and that's really helped me is just having that mindset and knowing that i can ask for help wow your data analysis toolbox is getting full learning about both spreadsheets and sql will get you far in the world of data analysis there's more to learn of course and lots more tools you'll be able to use but your future is looking bright and it's about to look even brighter because we're here to talk more about data visualization i'll tell you a little more about the role of data visualization tools in data analytics and give you a chance to see those tools in action later in this video you might remember that data visualization is the graphical representation of information for tons of data analysts it's the most exciting part of their job because they get to see their hard work pay off with something interesting not to mention that data visualization is beautiful and useful i was floored when i got to google and started to get a quarterly data report in my email it had a big slide deck where people contributed their visualizations it was definitely a source of light as i started to build my own visualizations if you're not impressed by my story let me tell you about florence nightingale does that name ring a bell she's responsible for much of the philosophy of modern nursing and believe it or not she was also a data analyst during the crimean war in the 1850s thousands of soldiers were dying every day nightingale wanted to find a way to reduce the number of deaths after examining the data she found that the majority of soldiers were dying from preventable conditions to convince hospital administrators that they needed to focus on these conditions she created a chart showing the number of deaths over several months the much larger blue sections individualization represent the preventable deaths her work directly led to major changes in patient care and she did all of this over 150 years ago without a computer one of the main reasons nightingale created this visualization was to make the data easier to digest for her audience she felt she'd be more successful convincing the stakeholders using visuals instead of just words and numbers she was right tables filled with data while necessary for analysis just aren't able to show trends and patterns as quickly and clearly as visualizations can imagine you receive an assignment that needs to be completed the same day you gather the data you need in a table could you explain your findings using the table yes you probably could but a better idea would be to use a visualization like this bar graph something like this makes it much easier for you to explain quickly and you've got the benefit of a cool graphic to back up your analysis as a data analyst you'll want to create visualizations that make the data easy to understand and interesting to look at so show it off stakeholders may not have much time to devote to the data your job will be to make their time worthwhile let's go back to that data table we created earlier in the course if you created your own for practice you can open it up now or try this out later here's the data we added before let's create a visualization of the data by inserting a chart a bar graph boom you can see that the spreadsheet visualized the data from our table in a way that made the most sense it created a bar graph or column chart to compare the ages of each person by name but you might have figured that out already that's the beauty of visualization it shows data analysis quickly and clearly we can use chart editor to adjust the chart different spreadsheet programs might have different ways to do this but they all have visualization functions and ways to edit those visualizations alright for now let's just look at the suggested charts we can make the bars go horizontally using a bar chart that looks great so let's close the chart editor there are lots of options to look at but we'll keep it basic for now feel free to try other visualizations if you practice later now we can adjust our chart to make our whole spreadsheet look clean and professional excellent i hope you learn to love data visualization as much as i do maybe you'll become a data visualization pioneer just like florence nightingale as a budding data analyst you've started to feel your utility built with valuable tools that you'll use throughout the rest of the program having spreadsheets sql and data visualization know-how will help make you an ace data detective you'll be able to use these tools throughout the data analytics process as you move forward coming up next you'll complete a few activities to wrap up this part of the program you'll also complete an assessment to check your understanding of all that you learn this is a great opportunity to think about some of the areas that you'll continue to explore in this course and in your career as always feel free to review the videos and readings to help remind you of certain topics and ideas even if you already feel prepared you're just a few steps away from the next course that's great progress keep it up my name is lyla jones and i am a part of our cloud team i get a chance to lead a team of amazing individuals that are focused on helping customers get to the cloud data visualizations that's a long word and that can also make your eyes glaze over but i wonder if when you were little and you were with your parents maybe they had a bedtime routine or maybe you have children you're doing a bedtime routine with them you very rarely are going to come to those children with a bunch of facts and figures before they go to bed but i bet you probably are telling them a story you're showing them pictures i know i always loved comic books pictures tell a story data visualizations are pictures they are a wonderful way to take very basic ideas around data and data points and make them come alive you can do all different types of combinations of visualizations but the ones that are interactive wow those are huge can you imagine being executive in organization and trying to figure out wow should we open up another site in in bangkok does that make sense and us being able to walk in and saying here's why it makes sense and having great data visualizations to support all of our points of view makes it a no-brainer interestingly enough i do recall the first time i came across a super amazing visualization it was in my personal life i switched my budgeting software from one provider to another and the provider that i switched to was really focused on every dollar has a job and making sure you're budgeting every single dollar they gave visualizations that change depending on what input you would add to it and it really just changed my entire perspective the entire thing so having the data is like having the answer sheet for a test it really just lets you know that you're going to make good decisions because it's backed up by data hi in this video we'll be taking a look how you can access quick labs from within your coursera user interface let's take a look here we are and you're working your way through a course and you see inside of your left side bar a graded external tool now first of all you should get excited that's where you're actually going to apply a lot of these concepts that you learned hands-on inside of our quick labs but how do you get from courseware to quick labs it's pretty easy it'll take us just about a minute so what you're going to do you land on this page we have some tips and tricks here for you and i'll walk you through one key important one the number one thing that i normally get wrong when i'm recording these videos or taking coursera courses with quick labs myself is that you want to make sure that you're in a private browsing or an incognito mode window inside of your browser why would you want to do that well if you're logged into corsair with incognito and keep in mind you might have to go through a captcha by selecting some images to make sure that you're not a robot before you go through it allows you not to accidentally make the mistake of logging into quick labs or coursera with any email that you don't intend to so once you're here and making sure that you're in an incognito window and if you're using chrome in the upper right hand corner you can just verify there's this little incognito icon here then you're good to go all you got to do is scroll down at the bottom click the i understand box add your initials and the most important thing that you're going to see is open tool in the lower right hand corner this button will become a nice dark blue once you've checked that button you open the tool and you'll see a new browser window come up now unless you want to hear my voice again you can just click skip this video because that's just an introduction there's also an x in the upper right hand corner for you which will then take you into the quick lab itself so to do your work you should now have two different tabs in your browser one has all the video lectures inside of coursera and the other has the actual hands-on quick lab and that's gonna be the subject of our next video where you learn all about how you can get credit for your work done inside of the quick labs we'll see you there welcome to quick labs now it's time to get hands-on practice on a lot of the amazing data analyst concepts that you've learned so far here's where you get to prove that you've learned some great technologies and then you'll get credit for it back inside of coursera but before you jump right in let me just give you a quick walkthrough of what a quick lab is and then we'll get started first and foremost in the upper left-hand corner you notice a gigantic tempting button called start lab in green i want to go ahead and start that click this box that says i'm not a robot select a few of the different items for your for captures you can see this is the hardest part of the lab is trying to actually get through the anti-robot technology that they have here now a couple things happen in the background as author of these labs this is some of our best work that we're getting you hands-on practice for and what we're going to do next is just make sure that that timer starts counting down because then that's going to give you the account logins that you need to practice the work inside the lab now here's what you're gonna do there's gonna be a gigantic big button that says open google cloud console i want you to go ahead and click on that and now you have three browser tab windows open or a hundred if you're like me and in the second browser this is your quick labs login all the way under the username what i want you to do is copy that username and here's the number one mistake that i've seen students make when you're back into google everyone sees this google sign in screen here and they immediately start typing in their personal gmail address you want to be charged for any of the resources that you can be using that's why we're providing you with this quick lapse account yourself all i'm going to do is paste in the email that's been given to me by the quick lab for a use for an hour and then click next and then i'm going to go ahead and grab the password paste that in here and then you're going to see a bunch of terms and conditions because this is a brand new account scroll through the terms and conditions read them at your leisure click accept and as you work through the terms of service here eventually you'll land on the home page for google cloud scroll down read the terms of service accept terms of service all the steps that i'm walking you through right now are also available inside of the lab written instructions as well so once you've gotten that out of the way let's take a look at what this particular quick lab is going to teach you at a high level i'm not going to do the lab for you but don't worry it's step by step you'll have a blast doing it and you have way more than enough time you need in this timer before your lab timer runs out generally as lab authors we try to double the budget of allowed time so don't worry about lab timeouts so inside the lab every lab will start with an overview and then what specifically it is that you're going to do inside this lab since you're a data analyst you'll be creating spreadsheets and adding files and sharing files with some of your other data analysts if you forget what section you're in another pro tip that i like to do is on the right hand side you can actually click between each of the different header sections right here as i'm doing now and you can jump to a particular section inside of the workbook so say you and your friends are working on this together you can say hey i'm having trouble on the share and collaborate section you can just click on that and it'll jump right to that section well that's pretty much it a few more housekeeping items and then you're off to the races as you work your way through the lab you want to make sure that you're completing the objectives and making sure that you're staying on track there is intelligence built into quick labs to prevent any kind of behavior that's not part of the lab so make sure you're following the lab as is and when you're done with the lab say i was completed here all you have to do is click on end lab click on ok and that's going to bring you to a pop-up screen that asks you to rate the lab add in any comments for the lab authors like me submit it and then automatically your score for the work that you've done in the lab is fed back to coursera which is pretty cool and that's it that was a quick tour of quick labs about five minutes but honestly you'll have a blast going through these good luck [Music] next we're going to cover a really important topic which is how to get help with quick lab should you need it and to do that we're going to jump back into our quick labs user interface here i am back inside of the quick lab inside the quick labs user interface if you're working through a lab and just something is not acting right or the lab instructions don't seem to be telling you exactly what you need to do we try our hardest to make sure that the labs are step by step but at any point in the lower right-hand corner you can click on the chat bubble once we've done that add in your name add in your email and you can optionally define the department for quick labs i'm going to say quick labs general support and you can start a chat so let me go ahead and add my name add my email and start chat and then this window will pop up now this gets you connected with the quick labs live support representative they're amazing they're friendly they've been doing this for years and likely your problem has already been encountered by somebody before so don't hesitate drop them a line and if they're out of office it'll automatically create a support ticket where they'll follow up with you from that email address that you provided that's it have fun working through your labs and earning that credit hey great to have you back now it's time to get down to business we're going to start talking about practical ways businesses are using data and the opportunities that it can create for you so far you've learned a lot of practical data analysis skills with these next couple of videos we're going to switch gears a little and talk about why you're learning these skills hopefully this will give you more perspective into what kinds of opportunities are out there for you coming up we're going to talk more about the kinds of roles data analysts play in different industries the tasks that these roles require and the importance of fairness and avoiding bias and analyzing data for business tasks we'll also talk about opportunities you can tap into and how this program factors into your future success in the data analyst role so with all that in mind let's get started previously we learned about what a data analyst does and why that work is so valuable now let's look at where data analysts actually do their work you'll learn much more about the industries you could work in as a data analyst and how companies in these fields are already using data analytics to do some really cool things there are so many businesses out there that have a big need for the skills you're learning right now across industries like technology marketing finance health care and so many more real companies are already using data analytics to stay ahead of the curve and the more they use data in their business the more they understand just how important data analysts like you are to their success let's look at a real life example of a brand you'll probably recognize coca-cola data is changing the way coca-cola approaches its marketing strategies coca-cola uses data gathered from consumer feedback to create advertising that speaks directly to different audiences with different interests how does this work you know those high-tech coca-cola vending machines you see at movie theater sometimes it's always fun getting to make your own flavors well those machines have built-in artificial intelligence and data analysis tools this helps coca-cola see all the different kinds of flavor combinations people are coming up with which they can then use as inspiration for new products how cool is that ever wonder how google gives you the right answer to any question in just seconds that's powered by data2 we use all kinds of data to determine a website's reliability and accuracy to make sure you get the most useful results for any search you make but it isn't just big companies like coca-cola and google that use data small businesses everywhere are also starting to take advantage of data-driven insights to improve their operations and make better decisions small businesses can use data to do all kinds of things they might use data analytics to better understand their customers buying habits create more effective social media messaging or in the case of one city zoo and aquarium predict the number of daily visitors based on local climate data city zoo and aquarium realized that on rainy days they were seeing huge drop-offs in attendance but they had no way to accurately predict when those rainy days would hit this made staffing a real challenge some days they found themselves overstaffed other days they were unprepared for the rush of visitors to deal with this data analysts took years of weather records from the zoo and used that data to accurately predict future weather patterns this made it easy for the zoo to know how much staff they needed when because the zoo could predict and manage their staffing needs more accurately they were able to provide a better experience for visitors and dedicate more resources to creating a better experience for the animals too we see a similar thing in the healthcare industry there data analysts look at clinic attendance data to help hospitals and doctors offices predict when rush hours will hit so they can be ready for it your local city hospital is a great example let's say they've been getting complaints about long wait times sometimes an hour or more which made it hard for some patients to get the care they needed so data analysts use data about the hospital's daily foot traffic to help them make more informed decisions about how many doctors they need on staff at any given time this helped reduce wait times improve their patients experience and make better use of the healthcare workers time too like i said there are many ways that companies in different industries put data to use but they can only do that if they have data analysts they can rely on so you might be wondering how you fit into the equation well you've got plenty of options but you don't have to decide what industry you want to work in right away there will be plenty of time to think about that as you make your way through this program by the time you finish this program you'll have the core skills that will make you valuable in any industry that makes data driven decisions which as it turns out is most industries even zoos coming up we'll check out the business task where data can be helpful and we'll explore even more how data analysts are empowering businesses through data i'll see you then hi i'm joey and i work as an analytics program manager within ruse now ruse stands for real estate and workplace services and my job is to bring data and analytics into the decision making here especially with regards to creating a safe and fun work environment my journey into analytics was a bit different in that i had no plan or really didn't see myself being where i am now now luckily i started in a rotational program called the hra program within people operations which afforded me the ability to play three different roles essentially i was in a generalist capacity in a specialist role and as an analyst and i really found a love and a passion in the analytical work i started on the business intelligence team whose job was to provide sql-based reporting back to the business i realized that analytics is the right career path for me when i found myself enjoying coming to work and getting my work done and i think i can connect that to two passions of mine the first is problem solving i love taking a complex problem a mystery a riddle and being able to find the answers and come up with the solution and then the second thing is being able to work with people and help people in analytics i feel like the key to success is being able to blend the personal side with the technical side at the beginning of my career i focused a little more on the technical pieces and i wanted to make sure i had the right technical knowledge to be able to answer questions but what i found is over time i needed to grow that other side just as much and i think that my career has allowed me those opportunities to kind of work each of those muscles the human interaction part and the technical part to make sure that they're both growing at the end of the day for any analyst for any person that's honestly at the early stages of their career understanding data respecting data and knowing how to work with data is incredibly important because you know my vision is that every role in some form or fashion will involve data and its use in learning how to extract insights from it will be at the core of any critical role across any company organization generally in those first two years you're developing the core skill sets that make you a fantastic generalist and then in the next two to five years you're learning about something very specific as it relates to your job so whether it's the area that you're supporting or maybe a very technical component like let's say you want to become a sql expert so that you can manipulate large data sets for financial analysis purposes similarly even if you come into finance as a data analyst you can pop out of finance and go into what a lot of people like to call the business which is typically your operations functions and become a business analyst or a data analyst there's so many different paths that you can take from the starting point that you really can't predict you're in i'm just deeply passionate about working with and supporting young people and really giving them a jump start to their career um this stems from honestly my own personal experience where in the first two years of my career i had essentially zero support from my manager and my direct management chains having gone through that experience in my first two years i realized and i felt experience how that can slow you down and especially when you're somebody that has a lot of potential and a lot of ability you want to be able to be in an environment that fosters that ability and really wants to see you grow i think it's incredibly important to have programs like these that take away all the barriers that remove any of the constructs that prevent people from being able to find out what they need to be in an industry like this to be successful in a role like a data analyst so that they themselves can dream about where they can go in their career my name is tony i'm a finance program manager at google as a data analyst you'll be tackling business tasks that help companies use data coming up we'll talk more about what a business task actually is and some examples of what they might look like in real data analyst jobs let's take a second to think back on the real examples of businesses using data analytics and their operation we've seen before you might have noticed a common theme across every example they all have issues to explore questions to answer or problems to solve it's easy for these things to get mixed up so here's a way to keep them straight when we talk about them in data analytics an issue is a topic or subject to investigate a question is designed to discover information and a problem is an obstacle or complication that needs to be worked out coca-cola had a question about new products data analysis gave them insights into new flavors customers already like the city zoo and aquarium had a problem with staffing data help them figure out the best staffing strategy these questions and problems become the foundation for all kinds of business tasks that you'll help solve as a data analyst a business task is the question or problem data analysis answers for business this is where you'll focus a lot of your efforts in the work you'll do for future employers let's stick with our zoo example and see if we can imagine what a business task for a zoo might look like we know the problem unpredictable weather was making it hard for the zoo to anticipate staffing needs so maybe the business task could be something like analyze weather data from the last decade to identify predictable patterns the data analysts could then plan out the best way to gather analyze and present the data needed to solve this task and meet the zoo's goals then using data the zoo would be able to make informed decisions about their daily staffing so we talked a little about data-driven decision-making in previous videos but just in case you need a refresher here it is data-driven decision-making is when facts that have been discovered through data analysis are used to guide business strategy the simplest way to think about decision making is that it's a choice between consequences good bad or a combination of both in our zoo example the zoo had the data they needed to make an informed decision that solved their problem but what if they had made this decision without data let's say they just relied on observation and memory to track the weather and make staffing schedules well we already know that wouldn't have solved their problem long term data analytics gave them the information they needed to find the best possible solution to their problem that's the power of data observation and intuition are powerful tools in decision making but they can only take us so far when we make decisions based on just observation and gut feelings we're only seeing part of the picture data helps us see the whole thing with data we have a complete picture of the problem and its causes which lets us find new and surprising solutions we never would have been able to see before data analytics helps businesses make better decisions and it all starts with a business task and the question it's trying to answer with the skills you'll learn throughout this program you'll be able to ask the right questions plan out the best way to gather and analyze data and then present it visually to arm your team so they can make an informed data-driven decision and that makes you critical to the success of any business you work for data is a powerful tool and with great power comes well you know the rest and you're doing a super job taking in all of this information up next we'll talk about your responsibility as a data analyst to make sure you're gathering analyzing and presenting data in a way that's fair to the people being represented by that data hi my name is rachel and i'm the business systems and analytics lead at verily there are a lot of different types of problems that a data analyst can solve i've been lucky enough over my career to have to have seen a lot of them and to take in a lot of very different types of data and help turn that into meaningful answers i think one of the most important things to remember about data analytics is that data is data i'm a finance data analyst and so my role at verily is to take all of our financial information all of the information of the money we're spending and the money we're making and turn that into reports and insights so that our business leads can understand what we're doing one of the most important things i've done it fairly recently was help create what's called a profit and loss statement for each of our business units and that means that in real time our teams can see what their budget is and how they're spending against that budget and what that does is that helps our teams keep to that budget by either increasing their revenue streams so that they have more money to play with or pulling back their spending so that they can keep themselves within that budget and all of that really helps keep us on track as a company and making sure that we're hitting our goals i've found that data acts like a living and breathing thing when you have a ton of data points it can be overwhelming when you first sit down to make sense of it you've have tons of columns tons of records tons of different types of data and finding a way to make sense of that is really hard and that's where the expertise of a data analyst comes in it has been some of the most frustrating moments of my career but also some of the most rewarding work i've ever done when it finally comes together the best advice i have for any data analyst starting out is keep at it if the angle you're taking doesn't work try to find another one try to come at it in a different way try to ask a different question eventually the data will yield and you'll get the insights you're looking for so far we've covered the different roles data analysts play in business environments and the kinds of tasks that come with those roles but data analysts have another important responsibility making sure their analyses are fair now i know what you're probably thinking data is based on collected facts how can it be unfair well that's a good question so let's learn what fairness means when we talk about data analysis and why it's important for you as an analyst to keep in mind fairness means ensuring that your analysis doesn't create or reinforce bias in other words as a data analyst you want to help create systems that are fair and inclusive to everyone sounds simple enough well here's the tough part about fairness and data analytics there isn't one standard definition of it but hopefully the way we've just described it can give you one way to think about fairness for right now but it's about to get a bit trickier sometimes conclusions based on data can be true and unfair what can you do then well let's find out with an example let's say we have a company that's notorious for being kind of a boys club it's very male dominated and there aren't many women employees this company wants to see which employees are doing well so they start gathering data on employee performance and their own company culture the data shows that women just aren't succeeding as often as men in their company their conclusion that they should hire fewer women after all women are doing poorly here right but that's not a fair conclusion for a couple of reasons first it doesn't even consider all of the available data on company culture so it paints an incomplete picture second it doesn't think about the other surrounding factors that impact the data or in other words the conclusion doesn't consider the difficulties women have trying to navigate a toxic work environment if the company only looks at this conclusion they won't acknowledge and address how harmful their culture is and they won't understand why women are set up to fail within it that's why it's important to keep fairness in mind when analyzing data the conclusion that women aren't succeeding in this company is true but it ignores the other systemic factors that are contributing to this problem but don't worry there's a way to make a fair conclusion here an ethical data analyst could look at the data gathered and conclude that the company culture is preventing women from succeeding and the company needs to address these problems to boost performance see how this conclusion paints a much more complete and fair picture it recognizes the fact that women aren't doing as well in this company and factors in why that could be instead of discriminating against women applicants in the future as a data analyst it's your responsibility to make sure your analysis is fair and factors in the complicated social context that could create bias in your conclusions it's important to think about fairness from the moment you start collecting data for a business task to the time you present your conclusions to your stakeholders we'll learn more about bias in the data analysis process later on in another course for now let's check out an example of a data analysis that does a good job of considering fairness in its conclusion a team of harvard data scientists were developing a mobile platform to track patients at risk of cardiovascular disease in an area of the united states called the stroke belt it's important to call out that there were a variety of reasons people living in this area might be more at risk with that in mind these data scientists recognize that fairness needed to be a priority for this project so they built fairness into their models the team took several fairness measures to make sure they were being as fair as possible when examining sensitive and potentially biased data first they teamed analysts with social scientists who could provide insights on human bias and the social context that created them they also collected self-reported data in a separate system to avoid the potential for racial bias which might skew the results of their study and unfairly represent patients and to make sure their sample population was representative they oversampled non-dominant groups to ensure their motto was including them it's clear that the team made fairness a top priority every step of the way this helped them collect data and create conclusions that didn't negatively impact the communities they were studying hopefully these examples have given you a better idea of what fairness means in data analysis but we're going to keep building on your understanding of fairness throughout this program and you'll get to practice with some activities hi i'm alex i'm a research scientist at google my team is called the ethical ai team and we're a group of folks that really are concerned not only about how ai and the technology operates but how it interacts with society and how it might help or harm marginalized communities so when we talk about data ethics we think about you know what is the good and right way of using data what are going to be ways that are going to be uses of data that are going to be beneficial to people when it comes to data ethics it's not just about minimizing harm but it's actually this this concept of beneficence how do we actually improve the lives of people by using data when we think about data ethics we're thinking about who's collecting the data why are they collecting it how are they collecting it and what for what purpose because of the way that organizations have imperatives to make money or to report to somebody or provide some kind of analysis we also have to keep strongly in mind how this is actually going to benefit people at the end of the day are the people represented in this data going to be benefited by this and i think that's the thing you never want to lose sight of as a data scientist or a data analyst i think aspiring data analysts need to keep in mind that a lot of the data that you're going to encounter is data that comes from people so at the end of the day data are people and you want to have a responsibility to those people that are represented in those data second is thinking about how to keep aspects of their data protected and private we don't want to go through our practice thinking about data instances as something we could just throw on the web no there needs to be considerations about how to keep that information and likenesses like their images or their voices or their or their text how do we keep that private we also need to think about how we can have mechanisms of giving users and giving consumers more control over their data it's not going to be sufficient just to say we collect elect all this data and trust us uh with all these data but we need to ensure that there's actionable ways in which people can consent to giving those data and ways that they can ask for it to be revoked or removed um so data is growing and at the same time we need to empower people to have control over their own data the future is that data is always growing we haven't seen any kind of evidence that data is actually shrinking and with the knowledge that data is growing these issues become more and more peaked and more and more important to think about by now we know that there are all kinds of jobs in different industries available for data analysts but now it's time to think about something just as important how can you tell if a job is a good fit for you and your career goals tough one right don't worry that's exactly what we'll cover in this video there's a lot of important factors to think about when searching for your dream job let's talk about some of the most common factors first industry tools location travel and culture data is already being used by countless industries in all kinds of different ways tech marketing finance health care the list goes on but one thing that's important to keep in mind every industry has specific data needs that have to be addressed differently by their data analysts the same revenue data can be used in three different ways by data analysts in three different industries financial services telecom and tech for example a finance analyst at a bank post public revenue data of telecom company x to create a forecast that predicts where revenues will be in the future to recommend the stock price the business analyst at telecom company x uses that same data to advise the sales team then a data analyst at the company who created a customer management tool for telecom company x will use that revenue data to determine how efficiently their software is performing finance telecom and tech all use data differently so they need analysts who have different skills it all comes down to what the needs of the industry are those needs will determine what kind of task you'll be given the questions you'll be answering and even how you'll approach job searching if you're just starting out a great way to guide your search is to think first about what you're interested in does helping people get healthier sound meaningful to you maybe you want to focus on using data to improve hospital admissions what about helping people save for a happy retirement you might want a job that uses data to determine risk factors and financial investments or maybe you're interested in helping journalism grow in your city a job using data to help find your local news website find more subscribers could be the perfect role for you the key is to think about your interest early in your job search that'll lead you in the right direction and it'll help you in interviews too potential employers will want to know why you're interested in their company and how you can address their needs so if you can speak about your motivation to work in data analytics during interviews you'll make yourself stand out in a great way you'll have options when it comes to where you work and who you work for but remember you want to enjoy what you do so it's a good idea to think about how you want to use your skills then search for jobs that allow you to do that next on the list of things to think about location and travel when you start your job search you need to make some decisions about where you want to live so it helps to ask yourself some questions does your preferred industry have opportunities in your area are you trying to stay local or would you be happy relocating how long are you willing to commute to work every day will you drive to work walk take public transport is that possible year round how do you feel about working remotely does working from home excite you or bore you and of course you'll want to consider cost of living and whether or not you want the convenience of city living or a quiet suburban home and it's not just about where you'll be based some jobs may ask you to travel which could be an exciting chance to see the world or a deal breaker it's all about what you want out of this job so start asking yourself some of these questions figuring out the answers can help you narrow down your search even further so you're only looking at jobs you'd actually accept once you've answered enough questions you'll be able to identify some specific companies that fit your needs at this point it's a good time to think about your values and what kind of company culture is a good fit for you ready here comes some more questions do you work best in a team or by yourself do you like to have a set routine or do you enjoy taking a new project and trying new things do your values match the company's values you'll want to pay attention to these things during your job search and interview process so you can be sure you fully invested in the company you work for that's the best way to start building an exciting and fulfilling career this program will help you learn the core skills for data analytics in any setting it's up to you where you want to take them whether that means starting in a completely new industry or moving into an analyst position in an industry you already have experience in and hopefully what we've covered here has helped you get on track for your future job search after this you'll have a few activities to do and then you'll be able to move on to the next part of this course we learned a lot so far like what kinds of opportunities are out there for data analysts in different industries how data analysts help businesses make better decisions the importance of fairness and data analytics and the potential questions you can start asking yourself before your future job search and you can always look back at these lessons if you want to review in an upcoming course we'll look at the skills all successful data analysts have and you'll learn how you can start practicing them too but before that you'll have an assessment good luck and i'll see you later my name is sama moid and i'm a recruiter here at google for the large customer sales team basically i hire talent for the sales team here even within the sales recruiting space i recruit specifically for the analytical lead roles here at google i want the candidate to be as comfortable as possible as a recruiter i'm also their advocate if they're a good fit for the team i'd like to present them in the best light as a recruiter some advice i would give for a data analyst that's just starting to look for a job think about a time where you've used data to solve a problem whether it's in your professional or personal projects another tip i would say for a data analyst that's just looking for a new a new job is to increase your professional network there are many ways to increase your professional network one of them is to increase your online footprint reach out to other analysts on linkedin join local meetups with other data scientists sometimes when we're looking for a really a unique skill set recruiters are going on websites like linkedin and github and trying to find that talent themselves it's really important to have your linkedin updated along with websites like github where you can showcase a lot of the data analyst projects you've done another tip i would say for an in-person interview is to prepare questions for the interviewer make sure they're not broad questions they should be questions that will help you understand the team and and the job better if you're given a case study in an interview you should expect to be given a business problem along with a sample data set then you'd be asked to take that sample data set analyze it and come up with a solution one of the things you can do to help prepare yourself for this is to ensure you are analyzing the data and coming up with a solution that relates back to that data sometimes there is no right answer and a lot of times interviewers are looking to see your thought process and the way you get to your solution i highly encourage that if you find a role that you're interested in not only apply to it but go the next step look for the recruiter look for the hiring manager online see if you can reach out to them and set up a coffee chat or send them your resume directly online applications could be a really big black hole where you never hear back from the recruiter or the team when you reach out directly to a hiring manager or recruiter it really shows your eagerness uh for the role and your interest for the role even if sometimes you don't get a response from that from reaching out you never know it's you know you try multiple different times and that one time you get a response back from a recruiter or hiring manager could be the time you get the job that you really wanted we're at the end of this course which means it's time to show off what you've learned we've covered the different kinds of industries using data to drive decisions and how you can help them how to promote fairness in your data work and opportunities that are out there in the world of data analytics i know you've got this and once you finish the course challenge i'll be right here to introduce you to the next course congratulations on finishing this first course you've already learned a lot and you're ready to take what you've learned and move forward and if you ever need a refresher just remember that these videos will still be here whenever you need them you might remember your next instructor from her introduction at the beginning of the intro course get ready to meet my fellow googler and your instructor for the next course ximena she's ready to help you get started on your next step towards finishing this program and becoming a data analyst this next course will build directly on some of the topics that you've learned so far and give you insight into the things we've already talked about like any good detective you'll learn how to ask the right questions and use data to find answers employees in every industry need to become comfortable asking questions but this can especially be true for data analysts a lot of data analysts try to make their work perfect the first time even though they might not have all the information instead of asking questions they make assumptions that can lead to mistakes it's so much better to be humble and inquisitive and to ask questions i'll show you what i mean one of the analysts i supervised came into google with no coding experience he was nervous about leaving a great first impression so he tried to study up on multiple languages by himself before he started when the work actually began he didn't ask us his team questions or ask us for help when he ran into roadblocks there are a lot of great places to find answers especially online and his initiative helped him find some of those places but at the end of the day he forgot to tap into his best resource us his team because he was nervous about how he would be perceived if he asked us for help he almost missed out on some great insights from his team members as roblox persisted he realized he needed to make a change he stopped trying to guess expectations processes and more all on his own and started asking us more questions as soon as he embraced this new approach he skyrocketed on our team his learning went straight up the curve like a hockey stick his impact on the organization the number of people who reach out to him and his career path going forward all did the same the bottom line is you don't need to know it all the saying is true there are no bad questions being open to learning is one of the most important qualities for a data analyst speaking of learning in the next course we'll go into more depth learning about basic spreadsheet skills and when you'll need to use them you'll discover how to apply structured thinking to data work and you'll focus on how to best meet stakeholder needs and expectations by gathering all the clues great work and good luck on the next course