Data science and data analytics. Are they the same thing? Well, you may have seen these terms used interchangeably, but if I'm mining data from a large data set, am I performing data science or am I performing data analytics? Or what if I'm trying to create a prediction of when my store will sell out of our current inventory of cantaloupes?
Well, is that analytics or is that science? It's worthwhile to understand the difference to better comprehend what these two fields can do and also if you're considering a career in either field. After all, these are two different jobs.
Somebody who works in the field of data science is known as a data scientist. For data analytics, that role is called a data analyst. Now, This is kind of a trick question because we can classify everything, data mining, data forecasting, and all the rest of it, as simply data science. And that's because data science is the overarching umbrella term that covers tasks related to finding patterns in large data sets, training machine learning models, and deploying AI applications. Data analytics, it could be argued, is one task that resides under the data science umbrella.
It's a specialization of data science, and it focuses on querying, interpreting, and visualizing data sets. Data science is iterative, meaning data scientists form hypotheses and experiments to see if a desired outcome can be achieved using available data. And that is a process that is known as the data science life cycle, which usually follows seven phases.
So first is to identify a problem or an opportunity. Then the next phase is data mining, which is to extract data relevant to that problem or opportunity from large data sets. Now, that data will likely consist of a bunch of redundancies and errors, which is fixed in the next stage, data cleaning. And then at that point, we move on to data exploration analysis. to try to make sense of that data.
We'll then apply feature engineering using domain knowledge to extract details from the data. And predictive modeling comes next to use the data to predict or forecast future outcomes and behaviors. And then finally, we have data visualization that represents the data points with graphical tools such as charts and animations, and so the life cycle repeats.
Now, the role of a data scientist is an in demand profession right now. If that's something you're interested in, you'll want to develop deep skills in machine learning and AI. It's helpful to be able to write code in languages such as Python.
Also in R is another popular language for data science. And you should have experience working with big data platforms. So perhaps Hadoop or Apache Spark. And it's also very helpful to have database knowledge and SQL.
So that's data science. But what about its specialization, data analytics? Well, the job of a data analyst is to conceptualize a data set as it currently exists.
So we have some data here and we need to do something with it. And we need to be able to make decisions based on this data. How do we conceptualize it? Well, Four ways. One is through predictive analytics, which helps to identify trends, correlations and causation within data sets like forecasting when those cantaloupes would have all flown off the shelves.
Or in health care to forecast regions which will experience a rise in flu cases. There's prescriptive analytics and that predicts likely outcomes and makes decision recommendations like predicting when a tire will wear out and need to be replaced. There's diagnostic analytics that helps pinpoint the reason an event occurred.
Manufacturers can analyze a failed component on an assembly line and figure out the reason behind its failure. And then there is descriptive analytics, which evaluates the qualities and quantities of a data set. A content streaming provider might use descriptive analytics to understand how many subscribers it's lost or how many it's gained over a given period of time and what content is being watched. And while a data scientist is a clearly defined and specialised role, virtually any stakeholder can be a data.
Analysts, for example, business analysts can use BI dashboards to conduct business analytics and visualise KPIs. But many organisations do employ professional, dedicated data analysts responsible for data wrangling and interpreting findings like why a company's marketing campaign didn't meet expectations. If you want to be a data analyst, it helps to have both analytical and programming skills.
So this includes familiarity with. databases. Also, you'll need to know about statistical analysis and also data visualization is another important skill. So data analytics is often more focused on using statistical tools and techniques to interpret existing data and offer actionable insights.
It's usually less concerned with creating new algorithms or models. Data science, on the other hand, has a broader scope that can involve complex machine learning algorithms often created from scratch. Data science focuses on phases from data collection to predictive modeling.
Data analysis, on the other hand, is more about answering specific questions with that data. And if you've done both your data science and data analytics right, you'll always be able to keep cantaloupes and just about everything else in stock.