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MyStory: I became a Data Scientist after working for 10 years in IT Industry

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Distant Memories (Prologue)

Getting into Analytics or Data science stream was never my dream. I got into this out of an accident. Prior to getting into data sciences, I was a mainframe programmer all through. The only aim that I had for a very long time was to get into a good MBA programme.


Business analytics as a new career Stream

It was 2013. I had 9+ years into software services industry by then. My career was almost stabilized and I could not see much growth out of it. Hence I was planning to turn up to my dreams of management education. But I was quite apprehensive as my long experience would bar me from getting into a decent MBA. It was when I came to know about the new upcoming stream called Business Analytics.

My first step towards getting into analytics was scouting for an opportunity internally. It didn’t worked out for me for two reasons. First, there were not much analytics initiatives happening within the companies at that time. Secondly, as the advanced analytics stream evolved as an extension to the business intelligence division in most of the companies (especially the Information technology services), they could not afford to take a non BI person like me and to train up for their analytics works. Also, there were not much courses online at that time. Or perhaps, I was not aware.


A Rude(Self) awakening

That was the time I decided to reskill myself to suit to the analytics industry. I had applied for couple of long term analytics programmes available in India and I managed to convert one of them into admission. But I never know that the class of business analytics will be math (advanced math) and technology heavy, the areas which I was bit apprehensive, until I went to the first day of my class. Apprehensive was due to the fact that I was working on a very old technology and doesn’t require you update constantly.

The extreme main skill that any data scientist should have – more than the art of storytelling – is the art of questioning and analyzing the information in hand. The very first mistake that I did before getting into business analytics was failing to understand what “Business analytics” professional do in their work – atleast its literal meaning. My cluttered mind strongly correlated “Business analytics” with the term “Business analysis”.  At the end of my first day class, one thing I really understood was – The decision to join the business analytics program was taken based on the gut feeling, rather than an informed decision.


Beginning of a thousand-mile

I had two stages of transition from being a mainframe programmer to getting into data sciences – First one was undergoing a one year programme and the other being the challenges that I face during the day to day work.


Advantages of structured transition

The one year analytics programme was extremely challenging – I would call it as a toughest challenge (yet most rewarding at a later point of time), I had till now in life. Multifaceted responsibility is the first challenge that I had faced as soon as I enrolled to the programme. Being a dad to a 5 year old, a production support lead at the office, coping up with the course was extremely difficult for me. With the kind of rigorousness that came along with the course, be it in terms of week end online classes (apart from the regular classes that we attend at the campus), tests, home works and assignments I was completely bombarded every day.

The one year spent at the programme was a roller coaster ride for all my student mates without any exception. The quality of problems that we were tested as part of assignments and their deadlines driven us to spend sleepless nights.  The effort to put completing every mini project, the early morning hour classes, the late night discussions  along with my family and office responsibilities – all together  would have made my health worse, if had I not been supported by the awesome peer group.  I still remember the days attending an hour of office call and taking up the exam in tandem. But I didn’t realize God was preparing me for the challenges that would follow up later in my career in analytics.


The First Date

Job interviews are always like first date. The outcomes are seldom predictable.

The next challenge that I faced was when I started looking for an opportunity to work in data science.  Hiring managers are typically concerned about taking in someone who comes with considerable experience in non-data science stream.  Luckily, the capstone project that I did as part of the course work with one of the prominent retail brands came in handy for me. The interviewer typically liked the process that was followed in building up the solution during this project. Thanks to awesome professors who mentored continuously.

These days, I also see many people who apply and get selected in interviews just by participating actively in data science competitions. Infact, as far as I have seen, data science competitors outshined the one with real time work experience in the interviews.


The Swell is really big

If the challenges pertaining during the academic stage of my transition was pertaining to the mathematical part, at work I was facing the challenges from the process and domain front. Here with I am briefing some of the lessons that I learned as part of my journey:

Be good in domain – One of the foremost challenge that any data scientist would face is the short-come in domain knowledge.  It is imperative for any data scientist to be good in the domain without which an analytics engagement can never be successful. Without the domain knowledge, a data scientist would never be able to answer any of the business question. He/She cannot build a good predictive model without the right set of variables. The insights that he generates will go in vain without the business knowledge.

Setting the expectation right with the customer – Most important and vital to satisfy every stakeholder. Customers may not know about analytics. All they know is that if the data is given, data scientists do some magic and generate insights which can be used to improve the business.

 A data scientist should exhibit patience and teach them what can be possible with data science and what not. I remember the days I spent with the customer explaining why a statistical model is much better than predicting manually, what is mean by a model by itself, how to interpret confusion matrix etc.

Knowledge of SQL and databases – I had an initial assumption that it is the duty of the data engineer to extract the relevant data required for building the use cases. It is false. Companies may not be interested in investing one more person for pulling the data. A data scientist is expected to know how to extract the data that he requires, and transform the data to the required format.

Patience for analyzing the data– Data analysis has to be done iteratively from various perspectives.  We never know what kind of pattern exist inside the data. However, by following structured data analysis methods and little bit of patience, the pattern that the data follows can be identified.

Presenting the solution– In my view, this is more important than building a model. All the hard work put in by a data scientist can be showcased only in this area. Also it is important to know the context of to whom the presentation is made. We may not talk about that adjusted R square or an ROC curve to end user.

Be comfortable when you fail – Most of the work that I did during the initial days failed to larger extent. Every time I presented the solution to the customer, there was a high probability that it gets rejected. I looked stupid every time I made silly and big mistakes. But I eventually learned that all these are part and parcel of the career in data science.  Just try to have a mentor if you are new to the analytics projects or start small if you don’t have a mentor.


The Meta Critics:

Here are some the questions that you should ask yourself before getting into a career transition:

Why do you want to make a career shift? – I meet many peoples who come to me and say that they wanted to shift to analytics just because they have problem with existing job/manager/company etc. If this is the case with you, try to see if something can be made interesting in your current work. Changing the work stream may not solve your problem.

Shifting career with high work-ex:  If your overall experience is more than 7 years, please think thrice before changing stream.  Please remember that with more than 7 years of experience, you may not be allowed to experiment in your work rather you will be expected to deliver.

Why do you want to get into data science?  If the answer is that data sciences present lucrative opportunity, then you might want to think again!! You will be getting into a field where you can’t sail smooth atleast for few years down the line.  You will be expected to keep yourself updated very frequently and it is very difficult for many people whom I had seen, including myself.

What do you know about analytics to make a career in it? –  Test this by doing some self-learning. Try some online courses in coursera or edx or udacity to check whether you will be comfortable with data science. Typically, you should not take data science as your career if you are averse to mathematics or if you show dislike to databases and querying tools like SQL. If you are new to all these, check these at khanacademy.com (for math courses) and w3schools.


Dare to Dream

Life holds special magic for all those who are dare to dream beyond their abilities. Some tips for the people who would want to hold their dream and looking to get into data sciences.

  1. Do minimum level of self-analysis before getting into data sciences. Test your fitment for data sciences in this link.
  2. Try to speak to some of them who already transitioned to this field. Their experience can give you lot of insights to your new career. If you don’t know anyone in person, post your questions in forums like discuss. Someone will be there to help you out.
  3. Read and subscribe to articles in Analytics Vidhya, data science central before even committing to the pricy courses available. Follow forums like discuss, quora data science Also if possible try to take couple of 101 challenges from datahack (and later kaggle). You will get a first-hand experience of how to solve a data science problem in these portals. I learned more by solving the challenges than working in real time environment.
  4. Never believe in claims which promises to make you a data scientist in less than 3/6/12 months. Every career has its own path of learning curve and data science is no exception to it.


Foot notes

  1. This article was aimed mostly for the people who want to enter into data sciences / analytics with considerable work experience. The challenges mentioned here may not necessarily apply for a fresher or with less than 5 years’ of work experience.
  2. Data science / business analytics are used interchangeably.



  • Aditya says:

    Hi Karthe could you please suggest the details of the 1 year program you did.

    • Karthe says:

      It was with Indian School of Business (ISB), Hyderabad – Certificate programme in Business Analytics. Now it is a 15 month programme.

      • AC says:

        Agree Kathe, I am currently pursuing the course, our 1st term is over, yes it is a roller coaster ride with lot of maths, stats & machine learning down the way, it was nice reading your experience.

        • Karthe says:

          AC – Give the extra push that is required to keep the momentum up and running. I am sure you will remember this period for the years to come. Good Luck.

  • Shahnawaz says:

    Good post.I agree with most of your pointers.I have started engaging myself with online courses for data science few months. I don’t see technical knowledge as a hurdle but the biggest challenge is to understand the domain/industry.Could you please let me know what else do you did to acquire decent domain knowledge.Do capstone projects help you to leverage your domain knowledge? I tried to solve a couple of online challenge but was lost somewhere in the middle.Please suggest.

    • Karthe says:

      @Shahnawaz – I did my capstone project in retail domain and it was very new for me. The basic knowledge we acquired from the internet. However our project sponsor provided complete knowledge required for doing the project. The same applies to the project i did in work. There are certain domains we cant find much knowledge in the internet. That can be acquired from the organization providing the projects only.

      The online competitions are designed in such a way that the knowledge required for working on the competitions can be acquired from the internet itself,

      The other source that i relied upon is the “dummies series” . For example – Retail for dummies. However I dont invest much in these books 🙁

      Hope i have answered your question.

  • Gagan says:

    Thanks a Lot Karthe … Article written by you is Inspiring for me !!

  • Mallikarjun says:

    Thank you very much.. surely it will help me to take wise decision.:)

  • Mahesh Jadhav says:

    Hi Karthi
    First all Thanks for wonderful article which i was looking for from a long time.
    I have recently joined the CBA course offered by ISB -Aug 2016 batch. My story is similar to you, in the sense I am also currently working as Technical lead/Project lead basically a mainframe developer with 8 yrs experience currently working at BNY Mellon. I have done my Masters from IIT Roorkee in Physics and Bachelors in Mathis , Physics and Computer science. So in a sense Good at maths and research..I have fair knowledge of OOP and Databases I am very much interested in pursuing a career in data analytics and would like to establish myself as a data scientist . I am selected for the higher education program by my company where 50% course fees is sponsored by company.
    By the time I will complete this course i,e, Jan 2018 I will be having around 9.5 yrs exp in Mainframe mainly in Banking, domains like cards, mortgages and securities. With this profile of mine
    can you Please let me know what are the challenges which i need to be ready to face as someone from a non analytics background in finding a job in Non banking/ banking domain.
    What do you recommend about the project , should we do within/outside our company (if given a chocie) and with in the same domain we are currently working or in some other domain which may help us in finding a Good Job.
    Also do you recommend moving internally within the organisation if given scope or should we try outside understanding that within the organisation we may not get any hike after this transition.
    also can you please advise on which topics we need to focus more while we pursue this course at ISB
    I think you would be right person to advise on above queries, as more or less our story is similar.
    if possible can you share your email id.
    Mahesh Jadhav
    [email protected]


    • Karthe says:

      @Mahesh – Congrats for getting into the first batch of 15 month program offered by ISB. You will have certainly an edge over the previous batches as you can put to test everything that you learn in CBA program in your capstone project. +1 congrats for getting sponsored by your organization for 50% of the course fee. That says me that your organization considers you as a potential candidate for putting you in-front for their analytics practice.

      Given that you have spent lot of time in banking domain / capital markets, you will certainly have an advantage if you do your capstone project or if you align your post CBA career in the banking domain. Adding value to your current organization by taking up a capstone project will also give you an opportunity to shine within the organization, as it is considered best way to do risk free career shifts.

      Alternatively, if you want to have an exposure to other industries, use capstone project as your platform. You will have your batch mates from almost all the industries, You can try be part of a group to the industry in which you want to get exposed to.

      Everything that is being taught in the program will be important. You may never know where you will use it your career. Especially there are some advanced topics being taught which you will never get exposure anywhere outside.

      I am concerned to give my email id here, however if you have any questions you can post here or in the discuss forum. There are many people to help you with, Hope i have answered all your questions.

      Good luck for your career in data sciences.

  • Mahesh Jadhav says:

    Thanks karthe , for taking your valuable time to answer my queries. But there is one point which I wanted to know specifically.

    As a Mainframe developer what are the challenges one may face when he joins into this field
    At what level is one recruited if he has about 10 yrs non analytical experience-assuming he cracked the interview- what roles can he expect.,assuming he is above average in all the concepts he learnt.

    Learning and interest is one thing but I also want to know how much hike can one expect when he makes such a transition and what is expected of such a person.\

    How was your life when you made this transition , do you had a team with which you worked and gained some of the knowledge on Job or how it goes ..

    Pls advise.

    • Karthe says:

      Hi Mahesh,

      All subjective questions and hence difficult to answer in the forum ;). I am concerned that the answers might mislead the readers of the blog. So I am refraining answering in detail for these questions.

      Where and how you want to work, what role you want to play etc depends on the interest and the level of expertise you show. I am interested in programming, Also i wanted to get my hands dirty before moving further. Hence I chose to be a techie.

      I c an’t comment on the compensation / increase in salary part as it is not the theme of this write up. Also I shouldn’t mislead people.

      We were a team who worked together during the initial days of career. More than that, I had the support of AV community members who were instrumental in clearing the doubts I had from time to time.

      Inspite of all these we will have our own challenges, that is something we need to accept.

  • Mahesh says:

    Thanks Karthik..well said..those are really some valid points you said. Completely agree with you.

  • Sreekanth says:

    Hi Karthe,

    Am working as Middleware Consultant from last 2+ years and with Engineering background. I want to switch into Analytics domain. From last couple of weeks been searching for the same on internet. Am not able to trust online courses for my career transition. Am open for two options 1. ISB 2.Great Lakes, Pune.
    As am from Mumbai, ideally ISB (Hyderabad) wont be suitable due to distance and job commitments. Option available is Great Lakes, Pune. Could you please advise me whether am on right track to get into GreatLakes for business analytics or should i think about some other institutes. Am into big dilemma re from where i should pursue my analytic plans. Please shed some light on me to get into right trick. Thanks in advance!

  • Sameer Subhedar says:


    Your article is truly inspiring, I am inspired by it. I am planning to take up the IIIT Bangalore Program which is happening in partnership with UpGrad. It is an online course, The course which you took up was a full time residential program or an online course ??

    Thanks !!

    • Karthe says:

      @Sameer – Thanks for your kind words. The program that i took was a part time residential program followed by online classes. Most of the concepts were covered in the classroom with Tutorial classes being conducted online on week ends.

  • Arrif says:

    Well articulated Karthe,
    actually I had discussed with you on call before (~3 months back) regarding getting into data science.
    I’m following what you suggested to get into data science unit within my org.
    I’m in negotiation, hopefully I will get into it.

  • Vishwa says:

    Hello Mahesh,

    First of all congratulations and wish you many success in this new adventure, here are few questions that I need to ask, I am almost 18 years in IT, have been in multiple roles such as manager, coach, sr manager, quality head, delivery head have worked in both India and abroad and right now in a senior role but don’t find it a lot too interesting and learning has come down significantly.., I am bit of pseudo technical but have been largely into delivery management and facilitation roles. I know where the industry is heading today and I see a major challenge for folks who are into delivery and program management and whether its good or bad, the change in focus for IT is going to impact all the management people around, With 16 years I have seen a plenty of customers, have a black belt and lean certification under my hat. I have almost lost touch with programming and looking for complete shift to secure myself for the next 10-15 years, that’s when I got this site and read your post. I am not averse to mathematics but you can imagine that I haven’t done any mathematics other than using Minitab or statistics few years ago. what is your experience and input to shift the career to data science, do you think folks like me will be looked at if we decide to make a shift

  • Guddu says:

    I would like to address one thing which many of you have in your mind “Taking a course to transition into Analytics – Would it payoff?” Simple answer is Yes but you really need to think about the time it takes to get your returns. Let me elaborate with an example – Person A, has arnd 5 years of experience with Data (Business Intelligence, Reporting, Data warehousing, ETL, SQL, any sort of descriptive analytics etc.) wants to get into Data science and Person B, has arnd 5 yrs of exp. has done nothing related to Data, wants to get into Data science

    Year 2016 –
    For Person A – it is a lot easier for him to take up a course of Data Science and get into this field in an Organization by advancing his resume. He would also most likely to get a position and compensation at par if not more for such transition. Building up further on this would be straight forward. Hence it could start paying him off in may be less than 2 years. Year 2018.

    Year 2016-
    For Person B – having no knowledge of legacy analytics and taking up a course of Data Science is like taking an admission into 5th class without having run through the basic classes. He would have to struggle for long time to build the base and then come upto the speed of Data scientist’s expectations. One has to start as a fresher and have to compromise on compensation. It might take him more than 5 years to reach to a level when it actually starts paying off. Year 2021.

    Now imagine where would the Person A be in Year 2021. India is full of people like Person A and most of them are doing something to get into Data science.

  • Rajesh says:

    Hi Karthe,
    Fantastic post !!!
    Thanks for sharing as it can be inspirational for many like me who belong to Mainframes background and 10+ years of IT experience.
    I have 1 question : Since we are Mainframe programmer/lead,do we need to learn front end technologies too – Java/.Net/HTML5 or CSS3/ Hadoop.
    I believe a knowledge of SAS/R/Statistics/Machine Learning/SQL should be enough to move forward.
    Can you please advise other than these skills (and Attitude) do we need something else to land data science jobs. Also, what role should we targeting as considering 10 years of current experience.


    • Karthe says:

      @Rajesh – Thank you. You need not know the front end technologies hands on. However a high level knowledge (what is what level) on these might be helpful when you design the data science solutions. Java knowledge may be required for Hadoop, but Hadoop as a whole “may not” required to start your career. (But things are changing fast.. Companies might expect to have the knowledge/skill of handling big data as well)

      Statistics is the core knowledge required for Data science professionals, Machine learning is the skill built atop Statistics. So both are mandatory, SQL is essential skill that you may use to pull the data that you want to analyse. R/SAS/Python – These are all tools for processing the data. You can have one of the skill as a beginner.

      The other skills are discussed in the article, with domain taking the higher priority, Without which, everything will be ineffective.

  • Venkat says:

    Hi Karthe,

    Well written and unbiased article! Even when people tempted you to speak on salaries, etc. you managed well 🙂

    I am searching on AV regarding Analytics courses and stumbled upon your article. Can you please suggest me on consulting prospects after completing analytics course and 2-3 yrs work ex? Just to give you background, unlike most other members, I have a PhD in Mechanical Engineering and currently working as a Mechanical Designer / Analyst. Main reason I am planning to join Analytics is my interest in patterns in data and a bit of love with numbers! Down the lane I intend to offer consultancy / training services in Analytics from a Tier-2 city in India. I would like to know if I am thinking in right direction and making right assumptions regarding consultancy environment for analytics? Which domain can I serve better with knowledge in analytics. What is your advice in terms of which course to pick to have strong fundamentals and applied knowledge. Thank you for your time and advice.

    • Karthe says:

      Hi Venkat,

      Thanks for your kind words. I am afraid to say that I am not an expert in providing personal suggestions and I am very much concerned that advising on personal choices should not mislead you,

      With that disclaimer, I can say that your expertise in the field of mechanical engineering coupled with data analytics skills will be very useful for the industry, The problem i see widely now is the lack of domain knowledge for the data analysts or the lack of data analysis skills with the domain experts. The industry is expecting to have both these skills, even though the candidate is not required to be master of both. I believe your expertise in mechanical engineering will be more useful from domain perspective for the data analytics industry.

      If you have any specific career related queries that can be answered by experts, I suggest you post your your question in the discuss.analyticsvidhya.com, the forum run by Analytics vidhya. I will be happy to answer any generic question about the industry, that you may have.

      Thank you

  • Venkat says:

    Hi Karthe,

    Thank you for detailed and encouraging reply.. I will post my question on domain in the forum.

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