We thank Kunal Jain of Analytics Vidhya for contributing an article this week. Kunal is passionate about analytics and through his blog, where he share his learnings about analytics, he hopes to inspire and create an analytics community that is committed to learning and developing skills in analytics. In this guest post he breaks down some of the common myths about a career in Business analytics.

Some time back, I wrote an article on “How to start a career in Business Analytics?“. The article was well received by people who want to enter Business Analytics. It is still one of the most popular articles on Analytics Vidhya. In response to this article, I received a lot of queries about career in Analytics. While some of them were good queries, some of them were recurring myths.

Hence, I decided to do a follow up article. Not only so, in order to debunk these myths entirely, I decided to publish the articles in all relevant forums.

Here are the myths I received through queries / comments / emails and my take on them:

  • You need to be an engineer to start a career in Business Analytics: The truth is that you don’t. All you need is ability to think structurally and comfort with number crunching. As long as you can put structure to unstructured problems and perform back of the envelope calculations, you are as good as any analyst out there.

Having said that, companies prefer people from quantitative background, as they are expected to be better with numbers. By quantitative background, I mean people from any of these disciplines: Engineering, Economics, Maths, Statistics, Physics or MBAs with graduation in these fields.

  • Analytics is about working with large datasets / Companies work with big data day in and day out: This rosy picture is far from reality in most of the Organizations. Experts estimate penetration of big data to be in low single digit percentage among Organizations. Most of the time analytics team work on specific problems, which may or may not involve large datasets. The requirement of the role is to be able to put structure across unstructured problems and be able to use numbers to understand business and the changes required in strategy.
  • You need to be a programmer: I was a good C++ programmer when I started my career in Analytics. Sadly, none of those skills have been utilized in last 7 years and might not be utilized in future. You only need to learn programming for the tool you use for your analysis (e.g. SAS, R, SQL etc.), but you don’t need to be a good programmer beforehand to learn these. Also, most of these tools have a Graphical User Interface (GUI), which you can start using without knowing programming.
  • Learning Analytics is all about learning a tool (SAS / SPSS / other tool): A tool is just a tool to perform Analysis. It cannot perform analysis on its own. You need to understand the fundamentals required for performing analysis like:
    • What are the things you need to keep in mind while performing Regression?
    • What can you infer from the coefficients and outcome of t-tests?
    • How do you prove or dis-prove a business hypothesis?

Once you understand these, applying them through any tool can help you start your journey of Analytics.

  • Its difficult to find a job – In fact it is the other way around. Analytics industry is struggling with attrition and shortage of talent. According to the McKinsey Global Institute (In a May 2011 report): “By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.” If you have the right skills, you will be highly sought after (at least in the current market conditions).

Are there any other myths that you are aware of? In case you are, or are unsure whether it is a myth or a fact, please add it below.

Interested in a career in Data Science?
To learn more about Jigsaw’s Data Science with SAS Course – click here.
To learn more about Jigsaw’s Data Science with R Course – click here.
To learn more about Jigsaw’s Big Data Course – click here.
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