Learn Data analytics before switching to Data Science
Disclaimer : Not adding another item in the endless learning list about how to make a career in Data science, after reading this article probably you would think you are already applying data analytics in your existing job. The objective here is not to exhaust anyone by adding another skillset, but to highlight the importance of data analytics in the world of Data science.
What is Data Analytics ?
If I simplify Data analytics in one sentence, it could as simple as saying :
Data analytics is all about finding problems and communicating about them to the right people
Let me share an example to make it more concrete
Let’s assume an online retail platform has changed the color of Add to cart button in their website and soon after this change the count of daily orders has decreased, now decrease in the daily orders is a kind of business problem and it can be caused due to multiple reasons, changing color might not be the only reason but the key highlight here is understanding the importance of analysing the daily orders count (data). Without analysing it we would not be even able to identify the problem and find the root cause of this problem. In any organisation every day numerous business problems can surface and to find the important ones we need to use data analytics.
Once we have identified the problem communication is also needed and there are lot of ways to do it, it can be done through sending email, spreadsheet reports, building dashboards, web application etc. but I believe it’s just a matter of choice, if the stakeholder is happy with even a verbal communication then it’s fine to communicate verbally.
So broadly there are two steps in data analytics — i) Finding the problem ii) Communication. But don’t get carried away with this simple explanation, there are lot of other things which we need to do while doing data analytics, for example writing ETL, defining metrics, aligning stakeholders, creating reports, validating hypothesis etc.
Now the big question, how data analytics is linked with Data science ?
Since there is no universal job description available for Data science the way I understand about this role might vary from others, for me doing doing data science in industry means solving business problems using math and programming.
Let me share an example , assume a ride sharing platform is launching a discount campaign and they want to identify from their user base whom they should target to get the best out of the campaign. Now there are multiple solutions available for a Data Scientist to solve this problem but all of these solutions are broadly an application of math (machine learning, probability, numerical optimisation etc.) and programming. Interested readers can go through this link to understand one of these solutions in more detail.
You might be thinking how data analytics is involved here, so data analytics is not explicitly involved in the solution phase but it is required in the problem definition phase. In the example given above they first identified using data that currently the way they are running the discount campaign is not efficient and they needed better targeting to make the best out of their campaign. To identify this problem, they must have analysed various data like discount coupon conversion, increase in orders volume, net sales etc. So without doing data analytics, Data science is not feasible and there could be countless business problems faced by an organisation and only using data can help us in identifying the important ones which are worth to solve for.
After reading this article I hope I am able to convince the importance of Data analytics to all the aspiring Data scientist.
If you are thinking which one you should choose between Data analytics or Data science, I would say it depends on your interest between finding the problem or solving the problem, personally I try to involve myself in both.
Thanks for Reading
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