Data Science Vs Data Analytics – What’s The Job Role Difference?

Data Science Vs Data Analytics – What’s The Job Role Difference?


data science vs data analytics

Data Science vs Data Analytics is one of the most popular topics of discussion among college students worldwide. Data Science, Data Analytics, Artificial Intelligence, Machine Learning, all these terms have been in the vocabulary of companies and recruiters in the past few years. 2018 was the year where Data Science and Data Analytics jobs became prevalent in India also.

All this leads to the question, Data Science vs Data Analytics, what exactly do these job roles offer, and how do they differ?

Let us first take a look at some examples of each job roles and then analyze what makes them different.

Examples of Data Scientist Jobs 

E-commerce websites like Amazon/Flipkart hire data scientists to predict what product launches and sales will work. To write algorithms to discover risk patterns, and formulate return policies. To identify trends and work with product managers in drawing up business strategy.

IT and consulting companies like Accenture and Infosys hire data scientists to develop analytical and statistical models, and machine learning methods for their clients to develop business strategies.

Examples of Data Analytic Jobs 

E-commerce websites like Amazon/Flipkart or Retail stores like Walmart hire data analysts to understand buyer behavior and patterns. They dig into data about where the purchases are facing hurdles, figuring out how mobile apps are functioning, and what consumers like, to create better services.

Media websites like Hotstar hire data analysts to understand consumer behaviour to offer insights to their advertising partners. To understand what kinds of users watch which shows/movies and what products can be marketed to them.


Data Science vs Data Analytics – The differences

  • Goal – A data science job role requires looking at the big picture. You have a set of raw data that you need to look at, make hypotheses and then draw conclusions in the form of statistical models. Whereas in a data analyst job role, you have to concentrate on the task at hand. The goal is well-defined, you don’t have to be too creative, you can work on the existing structured data and find out the answers you need.
  • Kind of data you use – One of the main factors while picking data science vs data analytics is the way you deal with data. In data science, you have huge amounts of raw and unprocessed data. You have to find your way out of it. While in data analytics, you are given specific processed data, the data sets are much smaller, and you can easily make sense of it.
  • Technical expertise – To get a data science job role you need to be a master of multiple tasks. You need to be technically sound plus be a creative thinker and have excellent communication skills. You will need to know the concepts of artificial intelligence, machine learning, and statistical modeling. In a data analyst job role, all you will need is a set of tools and software like R, Python, SQL, and any other industry-specific software.
  • Stakeholder interaction – In the debate of data science vs data analytics, the importance of each job role on the business strategy is of utmost importance. A data scientist works in tandem with other business roles like product managers, chief executives, and business consultants to provide them with insights and means to make decisions. In a data analyst job role, you don’t have to interact and work alongside them. You give them the report/analysis they are looking for, and you are done with your task!
  • Future prospects – The biggest thing that you need to understand about data science vs data analytics is that data analytics is just a subset of data sciences. You can start your career in a data analyst job role and gain industry experience, and technical expertise to upgrade your career into a data scientist job role. There is immense scope as a data scientist since you are experimenting with data, keeping up to date with the latest technologies and providing a window to the future with predictive analytics.

Now you can see that Data Science vs Data Analytics is not a to or for argument. You can very well start a fresher career as a data analyst and then move on to data scientist. You can find the right data analyst job on the job portal of AMCAT after writing the AMCAT exam and showcasing your eligibility to future employers.

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