I spent 24 hours researching the data job market and analyzing Trends and what I discovered shocked me it turns out my job might not be as safe as I thought and yours might not either the US Bureau of Labor Statistics predicts explosive growth for data scienes statistics and market research analysts in the coming 10 years much faster than Market average but in reality many people are struggling to find a data job and with AI shaking things up it's got me wondering how cure are our de jobs really what exactly has changed in the past one or two years in this video I'm going to share five key trends I've noticed in the market right now trust me there's some silver lining too first up let's talk about the job market you might be thinking with all the take layoffs we've been hearing about surely data jobs must be taking a hit right well you might be surprised since the end of 2022 the number of jobs for data scientists data analysts and data Engineers has decreased by about 15% but has remained quite stable since the beginning of the year this is pretty interesting especially when you consider that the tech industry has experienced two big waves of layoffs since the covid-19 one at the beginning of 2023 and another at the beginning of 2024 but if we lay this data job posting data next to the tech layoffs we can see that the data job posts didn't seem to be affected much by the take layoff Trends this stable Trend can also be observed per job title now I'm not saying it's all sunshine and rainbows compared to the last two years the data job market is certainly more competitive with companies being more selective the uncertain economic growth and high interest rates are making companies tighten their budgets and seek skills that focus on efficiency and cost reduction and this might explain why it feels harder to land a job today don't let that discourage you though it just means we need to be smarter about how we position ourselves in the job market which brings me to the next Point Let's now talk about technical skills if you've been in the data field for quite some years you've probably noticed that certain programming languages are becoming more dominant technical skills are consolidating and the data backs this up a whooping 86% of data scientists said that python is the main language they use for current projects another 10% said they use it as a secondary language that's a huge number and python is being used for everything from data analysis to machine learning and web development a few years ago if you asked me should I learn r or python I would have said it doesn't really matter which language you start with but today i' definitely say Python and by the way if you're curious about the visualizations I'm showing they were created on jet brain's data law platform this platform makes it super easy to crunch numbers on a notebook spot Trends and share insights in a team I've also used this built-in AI coding assistant to whip up some some cool graphs for my research it saved me hours of hair pulling over tiny chart details you can find the data story and dive deeper into the data behind this video on jet brain's data law blog check the link in description below thanks to Jet brain's data law for sponsoring this video that's it python isn't the only player in town looking at the job posting data over the last two years SQL consistently appears in up to 60% of all job posts right alongside python month after month this this shows that Python and SQL are and Will Remain the dominant languages for data jobs for a foreseeable future so if you're trying to become a data scientist from scratch today and are wondering what programming languages to learn i' say it's smart to focus on Python and s in fact you might be surprised to hear this but Wes mckin the creator of the panda package was recently asked to give advice for data scientists some people on the Discord are going to laugh because I'm going to say that like learning squel is actually a really good skill it's not just learning SQL the language but learning how to think about data sets and like designing schemas organizing data relational algebra and knowing how to use how to use data um execution engines data warehouses SQL engines file formats and like the basics of data storage data partitioning I think all of these things will enable you to be a better DBT user they'll enable you to be a better snowflake user or a data bricks user as much as I love python I must agree on this I once worked on a machine learning project at a large bank and we were building a machine learning model to classify customer risks for money laundering we used bpark SQL to query Bank transaction data think of billions of rows I remember at the beginning it took me like 10 minutes just to even run a commment to check the number of rows in the data it was ex cruciating only afterwards I learned about partitioning and load balancing and so forth to help me optimize my codes so if you're looking to level up your skills don't overlook SQL it's more than just a query language it's a way of thinking about data that can make you a better data professional overall now here's where things get really exciting while data scientists and data analyst roles are always in demand there's a new kit on the Block AI Engineers this role has emerged with the rapid developments of large language models in the past two years interestingly AI engineer role doesn't typically require a BHD rather it requires an in-depth knowledge of LMS prompt engineering an AI agent workflow engineering this role is still very new and there are different opinions about what the job exactly entails but in general you can think of it this way if we put the data science SL machine learning research on one end of the spectrum and AI applications on the other AI Engineers lean towards the product and user end they built applications that use pre-trained AI models or Foundation models to solve a specialized business problem let's say a company wants to develop an AI application like a specialized customer chatbot using our LMS AI Engineers will be the ones who put the AI model in place do some prompt engineering fine-tuning the model if necessary tailoring the workflow to the use case and also evaluating the application to make sure it works as it should Andre kathi predicts that in numbers there's probably going to be significantly more AI Engineers than there are machine learning Engineers or airm Engineers one can be quite successful in this role without ever training anything and the job post data backs this up in the past 2 years AI engineer jobs have been growing much faster than machine learning engineer jobs and have surpassed machine engineer jobs in May 2023 according to haen new hiring Trends you might be asking what's the key difference between AI engineers and machine learning Engineers well it's quite straightforward LM applications rely heavily on prompt engineering which of course doesn't exist in a traditional machine learning model evaluating LM applications also requires a very different approach to using precision and recall metrics or mean squared error like in machine learning models recently PWC one of the big four companies landed a deal with open AI to become its first resale partner and this partner partnership helps BC scale AI capabilities across businesses to help Drive accelerated impact for clients what this basically means is more and more businesses will be able to build and incorporate AI into their Solutions and who will be implementing all this well AI Engineers so if you have a data science background how do you become an AI engineer I'm certainly not an expert on this but someone on hien news suggested focusing on these areas firstly m medical foundations basic statistics Python Programming and he also mentions a bunch of courses such as machine learning specialization and deep learning specialization by Andre in on corera Fast AI deep learning courses and then you can choose a specific area of AI to focus on for example natural language processing computer vision reinforcement learning and other specializations recently I've also heard people talk about new roles like quality assurance business analysts or QA analyst in short who investigate LM outputs design AB tests and create dashboard to monitor the performance of the LM application this is yet to be seen in the job posting data but I feel like anyone with an analytical mind and solid data science skills can adapt themselves to this new role now let's talk about another interesting Trend the rise of freelancing in the data World in 2024 this seems to be a significant increase in the number of job posts looking for contractors and Freelancers this is exciting news for those of you who might be interested in a part-time job or more flexible job becoming a freelancer is also a great way to learn new skills fast and build diverse portfolio because you get to work on various business problems with clients sometimes in completely new domain areas with different types of data and analytical tools in the US most Freelancers find their work through previous clients friends and family social media and profession contexts the question is if you don't have a previous client already how do you find your first client here's my advice start small and utilize your own network post about your relevant project on LinkedIn show your skills and mention that you're looking for freelancing work in this and that area I myself used to be very scared to post stuff on LinkedIn but it's a good sign you're getting out of your comfort zone even better you can directly ask your neighbors friends and family members small business owners around the Corner might need your help and friends and relatives might also have interesting work for you you don't need to look very far focus on those you can reach once you have few small projects under your belt you can even start going on online job boards like upwork and Fiverr to find more jobs last but definitely not least let's talk about a trend that's changing the game for a lot of businesses and people loow code and no Cod tools it's not an exaggeration to say that in the future anyone could become a data analyst with without years of training and I'm talking about doing much more than just creating pivot tables in Excel low code and no code development platforms enabled by AI are becoming increasingly popular the average forecasted low code Market size is expected to grow about 23% from 2023 to 2030 these tools are making data analytics more accessible to people who might not have traditional coding skills or data science skills they provide simplified IND faes that let anyone Block in their data and do tasks like data preparation analysis data visualization and even build machine learning models without significant coding effort I know what some of you might be thinking this sounds too good to be true right well you're not entirely wrong look AI powered local tools are great but they are not a Magic Bullet AI is smart but it still needs human expertise to guide it in complex tasks and at the end of the day someone needs to make sense of the results evaluate them and make a decision and how this whole local development trend is impacting data jobs I think firstly these platforms are automating a lot of entrylevel data analysis tasks and this might hurt starters in the field looking for the first job but this also gives opportunities to newcomers who have some expertise but come from a non-tech background I think in the future jobs will probably become more specialized so domain knowledge is going to be more and more more important and so if you're looking for a job instead of just looking for data analysts or data scientist job titles you might consider a wider range of job titles such as marketing analyst sales analyst risk analyst psychom magican or quality assurance analyst or perhaps even data cleaning ninja I love this job no matter what your next career move will be the market will be ever changing and at the end of the day there are three things that really matter firstly be good at what you do companies still find it hard to find someone who is good even in a tight Market secondly prove it have a strong portfolio of projects including volunteering or freelancing experience and lastly let people know it through your networking skills the key is to stay adaptable keep learning and make valuable connections with people if you want to dive deeper into the data behind this video you can find the full report in a de law Notebook on the jet brains de law website link in the description thanks for watching Ing and I'll see you next video bye-bye [Music]