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Ashish Nanda is an alumnus from the Batch of 2017 who graduated from the Department of Engineering Physics. Ashish has experience of working as a Data Science Statistician at Axis Bank for a period of 3 years. He is currently pursuing an MBA in General Management from IIM Ahmedabad. Through the blog, he shares some valuable insights about what sparked his interest in Analytics, his journey, and exposure to the field:-  

Q1: How did you plan on entering analytics? What was your line of thought before deciding to enter this field?

  • Initially, I was considering to pursue research. It was only later that I realized I wasn’t interested in it.  
  • I had heard of Analytics. To me, it sounded really interesting, and I knew for a fact that it was an industry on the rise.
  • I could remember that only a few companies, like EXL, had come to recruit analysts during my time.

Q2: What is your specialization? (Big data, business intelligence, data science, predictive analytics,  etc.)

  • My specialization mainly involves Predictive Modelling. It incorporates the use of various tools like regression models, decision trees, neural networks, and other machine learning algorithms in order to achieve the results
  • I firmly believe that some knowledge of statistics comes in handy; familiarity and comfort with coding goes a long way in facilitating your work
  • (I also use a plethora of Data Science softwares like SAS, Kubernetes, etc.) 
  • A big chunk of my time goes into Data Wrangling and Data Modeling 

Q3: What was your level of preparation?

  • I did not have any formal knowledge of Predictive Analytics, except for the course I had on Data Interpretation.
  • As a part of my engineering curriculum, I had done a lot of Data Modeling and Quantitative Analysis.
  • My familiarity with coding was instrumental in convincing the recruiters that I am indeed, a potentially good candidate.
  • My Internship was related more to Physics than Analytics, but it had a good amount of Modeling & Statistical Analysis involved.
  • It so turned out that my fundamentals of analytics were pretty clear and I had a good grip on them.
  • I’ve had no experience of Hackathons since I work in a traditional sector, like banking. Hackathons are not necessary but with the kind of competition which is evident today, they do make you stand out of the crowd.

Q4: If you were to start all over again, what would be your roadmap to analytics?

  • There are several ways to enter the field. A good approach would be to do well in your Mathematics courses, especially Linear Algebra.
  • It’s important to do a lot of quantitative courses. One should take more such courses through ALCs, Electives, etc.
  • For instance, I had done the “Andrew Ng” course and a formal “Intro to Machine Learning” Course in my 3rd year.
  • It did make me stand out but with the rising interest in Machine Learning, it has become more important to do advance courses in college years.
  • The least you could do is to focus on the Data Analysis course that is taught to you in your curriculum. It does not matter when are you doing it- second, third or fourth year- but do your best to understand the concepts well. It is also important not to get too bogged down if such courses seem too heavy, it is definitely beneficial in the longer run.
  • One should also make use of online materials to get comfortable with Statistics because it is going to be an important part of your work

(Ashish suggests websites like statquest.org for the purpose)

Q5: Enlighten us about your corporate/workplace experience

  • The analytics division consists of people working across various sectors like Corporate Finance, Retail, and managing the internal data of the company. I personally work on credit risk analysis and fraud detection.
  • In terms of the roles, a major part of the team consists of Business Analysts and only a few selected people work as Predictive Analysts (refers to it as the “cool stuff’)
  • Usually, a team of 3 people is assigned to one project, i.e. 1 Manager and 2 Analysts.
  • 1-2 meetings per week are held to discuss the progress and plan ahead.
  • One gets to learn A LOT in the field. That’s always something to look up to. 
  • When I joined, the sector was still transitioning from hiring more “Mathy” statisticians to engineers, who tend to be good programmers. 
  • Just within 3 years, the field has rapidly smartened. The hot topic has changed from: “Do you know Python?”  to ” Do you know Kubernetes?”
  • Communication skills are also a good plus point and come in very handy at work. This is when I realized the value of PORs.

Q6: What’s next in store for your career/ job profile? Have you given a thought to any Future Plans or Exit Options?

  • Pursuing Analytics gave me good exposure to working in the financial sector. Working closely with my manager has motivated me to take up an MBA in General Management.
  • Even though Analytics didn’t have a direct role but just stating that I have worked as a data scientist sounds impressive to the interviewers, during admission

Q7: Any other words of wisdom/opinions/suggestions to be shared with the students?

  • I believe that students should have their goals clear and not just about whether to enter the field of analytics, but also about what do you exactly wish to do in this field.
  • They should consider various sectors and job roles parallelly. 
  • One should focus a lot on courses that are quantitative in nature and force you to derive results from experimental data.
  • Always be open to various opportunities while working, because analytics also has various exit options, ranging from management and  finance, to consulting, and even higher studies in data science.