How do I become a Business Analytics professional?I get this question frequently from both fresh graduates and experienced professionals. So I thought of writing an article that would help both the fresh and experienced professionals to get into the analytics industry. Now, let‘s dive into the approach.
Subscribe to our Business Analytics certification program.Step 1: Understand what a Business Analytics professional does. There is a huge demand for Business Analytics professionals in the technology and financial services industries. The recent Research and Markets report stated that the global big data IT spending in financial services market will grow 25.5% (CAGR) over the period 2014-2019. As there is a huge demand for Business Analytics professionals in the technology and financial services industry, I have divided these two industries into four categories. Financial services: HDFC, ICICI, American Express, MasterCards. IT service providers: CTS, TCS, Wipro, and Infosys. Internet companies: Google, Facebook, Amazon, Flipkart, Linkedin, Zomato, Paytm. Analytics service providers: Mu Sigma, Fractal Analytics, Crayon Data, Latent View Analytics, Absoultdata, ZS Associates, etc. Choose a list of companies from any one category, I have listed only a few companies above, there are lots of companies available in each category. Take one category and then shortlist some companies in that category. Use LinkedIn’s Advance Search feature to look for Business Analytics professionals in the companies you have chosen, and then send them a custom LinkedIn request. Something like this: I’m a big data enthusiast. I’m really impressed with your background and would love to learn what drew into Big Data. I’d love to keep in touch and learn more about your work. After they accept your request, send them a personal LinkedIn message stating that you would like to know how they got started in the field of analytics. Convey that you would like to get on a short call to know more about their job profile. Most of these professionals will be willing to help you. Talk to at least ten people to understand their job profile much better. Step 2 – Structured Thinking Structured thinking is the most important skill for an analytics professional to possess. Structured thinking is the process of developing a framework for an unstructured problem. So, you need to be able to design a process flow to complex business scenarios that you encounter in your everyday job. The best method to develop this skill is to analyze business case studies. Business case questions improves not only your structured thinking process but also your numerical ability and speed of thought. The book Case In Point has lots of case studies, and step by step solutions to these case studies. So, go through the book Case In Point at least once. Once you read the Case In Point book, next, read the book called Data Science for Business to develop your analytical thinking process. Data Science for business helps you to understand the underlying concepts of data science, and most importantly how to approach and be good at problem solving. The interesting thing is that the book shows you how data analytic techniques like k-means, logistic regression, linear regression are used to solve practical business problems. Step 3- Learn Linear algebra, multivariable calculus, statistics and machine learning Linear algebra and multivariable calculus are the foundations of data science. As a business analytics professional, you have to communicate frequently with data scientists. Data scientists are mathematical geeks, who would use terminologies like under fitting, overfitting, clustering, false positives, true negatives, etc. To understand these jargons you should have a very good understanding of statistics, machine learning algebra, and multivariable calculus. Here are some resources to learn these techniques: Linear algebra and multivariable calculus: Khan Academy. Statistics: Watch all the videos in the intro to statistics course on Khan Academy. Machine learning: Andrew Ng’s course. Just watch the week 1, 2, 3, 6, 7, and 8 videos – don’t do the assignments. Step 4 – Learn R or SAS Why R or SAS? Because there are more number of job postings for SAS and R, when compared to other data science programming languages. Choose either R or SAS and then learn the basics. Why don’t you check our R and SAS certification programs? Now it is time to work on our first data science problem. I recommend you to work on the Titanic machine learning problem because the problem is simple, and it gives a good understanding of what a typical data analytics project looks like. Trevor Stephens has written a blog post on how to solve the Titanic problem(the link points to the 4th part. You may want to navigate to the introduction part). Here is the GitHub repo to the problem. Use Trevor’s GitHub repo and blog posts as a reference and solve the problem. The most important interesting thing here is that you can rank yourself against other big data professionals on Kaggle to see where you stand. Step 5- Build data analytics projects You need to do more projects to demonstrate that you can walk through a data problem end-to-end: from data acquisition, cleaning, analysis, to communicating your findings that even the tech illiterate can follow along. Where to start? To begin with, there are lots of socially relevant data sets available online that you can analyze. Here are three specific examples of rich data pools you can pull from to examine data on a global, national, and hyperlocal level: World Bank: The World Bank Open Data project provides free and open access to thousands of datasets about development in countries around the globe U.S. Census: The U.S. Census provides ample potential for fascinating data insights. Slice and dice datasets on everything from population estimates per square mile to mean travel time to work The US States data: New York, San Francisco, Seattle and Philadelphia, and other cities have all made some subset of their city data publicly available, from public transportation and energy usage to school test scores and crime. Master list of interesting data sets found by some of the well-known data scientists in the industry. Have 4-5 good projects in your portfolio. So that you can speak about these projects during the interview. At this stage, I wouldn’t recommend you to participate in Kaggle competitions. As Kaggle competitions are very challenging. So it is better first to work on your portfolio and then participate in Kaggle competitions. Step 6 – Network and apply for jobs Truly, sometimes it’s who you know, rather than what you know, that can land you the dream job. And having the right professional network at your fingertips can expose you to more job opportunities than if you were trying to land a gig alone. A few good ways to build up your network of professional contacts: