Making a transition into data science is a journey paved with obstacles and learning. There is so much to learn and implement! This can get especially challenging if you’re coming from a non-technical background.
But isn’t that the great thing about learning? We get to experiment with concepts, apply them in a safe academic environment, and add to our knowledge through practical applications. The experience becomes even richer when you’ve worked in the corporate field for a number of years – everything seems novel and new again!
I recently made a career transition into data science. This article is my experience with this switch, my learning, and my advice to all aspiring future data science professionals looking for their first big break.
Table of Contents
- Background: Change was Necessary
- Career Move: Work on the Technology you Love
- What I Learned During my Transition into Data Science
- The Different Activities and Resources that Helped My Transition
- Challenges Faced During this Transition
- PRAXIS: The Beginning of New Challenges and Opportunities
- Campus Life: A Bag Full of Memories
- The First Analytics Job Offer
- The Next BIG Move: Learn and Grow
Background: Change was Necessary
Like the majority of students graduating with a B.Tech degree in Information Technology, I was placed in one of the top Indian multinational consulting companies in 2012. The future held so much promise for me!
I worked as an Application Developer in Java for a BFSI client for 4 years. However, my learning graph slowed down over this period. I was restless even though I was in a comfort zone. The role was great but I was looking for professional growth. So I decided it was finally time for a change.
I began preparing for GMAT to pursue a Master’s degree abroad. I cleared TOEFL and GMAT but unfortunately had to drop my plan due to some financial obligations. So, what next?
Career Move: Work on the Technology you Love
The best way of penetrating into a new field is first understanding the current technologies. The buzzwords back in 2016 were, as you might have guessed, ‘Data Science’ and ‘Machine Learning’.
I had vaguely heard about these terms through online articles. I started exploring career options in this field and found that Statistics was the base of Data Science. This meshed perfectly with my interests – Statistics has always fascinated me. There is nothing better than working in a field that you love!
A quick Google search on ‘Analytics Machine Learning Tutorials’ led me to India’s largest data science community, ‘Analytics Vidhya’. I went through their articles on educational institutions providing courses for careers in Data Science. A deep dive into this domain made me believe that Praxis Business School was the correct option for me.
This was an institution providing a full time analytics course with an industry-ready course structure, a set of renowned professors, comparatively low and manageable tuition fee, and of course, the successful placement records that Praxis had shown since its inception.
The last move was to have a quick conversation with Praxis alumni to understand out their experience. This convinced me to apply to the Praxis Business School Post Graduate Program in Business Analytics and fortunately I got selected!
What I learned During my Transition into Data Science?
I have spent most of my professional career in programming before switching to Data Science. I studied statistics as a kid but those concepts were long forgotten. Making this transition was indeed tough, but not impossible.
The key is to never stop learning. During this switch, I realized that you don’t need to unlearn your existing skills to pick up a new one. I used my programming skills as a bridge between IT and Data Science to structure my machine learning code more logically.
This transition also helped me understand that the presentation of project results varies significantly from industry to industry. For example, in the IT industry, the output of a web development project is a web page that is completely understandable by the stakeholders. In the world of data science, the output is (usually) numbers. It is the role of a data science professional to reveal these numbers to the customers/stakeholders using an indicative story.
For those who are ready to start your transition into Data Science, I recommend reading the below suggestions carefully:
- Ask yourself this – are you are really interested in data science and are you a good fit? Don’t just fall for the glamour and hype. There are plenty of resources available online, such as the various articles and blogs in Analytics Vidhya, to get a feel of what this field is all about
- Statisticians and programming professionals will undoubtedly have a bit of an advantage. But here’s the good news – the transition is possible even for non-technical people. The primary thing that matters is your thought process and skill of questioning and analyzing the information at hand
- If you are an experienced professional in any other sector, get ready to be treated as a relative fresher when you make the switch to data science. It might be quite difficult for many people to accept a transition where you have to forego some years of seniority. I understand that. But if you give your best, then this industry will bestow even more knowledge, greater successes and awesome salary hikes on you
The Different Activities and Resources that Helped my Transition
Despite the industry focused and well-structured curriculum that Praxis provides in its full-time program, it’s very essential to know where you stand in the world of Data Science. To get into the ecosystem, I participated in as many hackathons as possible, especially those organized by Analytics Vidhya and Kaggle.
These hackathons give a sense of real-life data science problems and also provide a leaderboard to compare yourself against the top data scientists. Even now, when I am into this field professionally, I try my best to attend the great meetups organized by Analytics Vidhya, Pycon India, etc. to name a few. These meetups are the best source of knowing what’s happening in Data Science around the world and meeting some great minds.
Also, I always read the blog section of AV – it comprises of the latest developments in Data Science, and also explains ML algorithms in simple and easy to understand language. Moreover, I have some great LinkedIn connections who share wonderful articles related to AI and ML regularly. I also used to refer to the UCI Machine Learning Repository which is another great source for datasets.
The resources are unlimited but you have to know how to search and find them as self-learning is the best thing that one can gift oneself.
Challenges Faced During this Transition
The biggest challenge is to stitch not one but many skills together to be an effective data scientist – skills like statistics, machine learning, databases, visualization techniques, programming and of course the art of storytelling. Also, there is always a new tool or package or an entire algorithm coming up every now and then.
It’s a tough job to cope with the speed with which this field is progressing. Therefore, in addition to the classroom studies, I used to devote an extra hour daily to read articles and blogs published in AV, Quora and LinkedIn so that I stay updated with the latest technologies. Also, I have reached out to the faculty team at Praxis in case of doubts with any concerned subjects and every time they proved to be very helpful.
PRAXIS: The beginning of new challenges and opportunities
Coming back to student life after spending 4 years in the corporate sector was quite a challenge. From the first day at Praxis, it was a known fact that the next one year was not going to be easy. A variety of nearly 25 different subjects distributed over 3 trimesters were covered during a span of nine months.
Most subjects were totally new to me. But I was hooked due to my previous programming experience and love for statistics. There were subjects in different domains, like:
- Basic and Advanced Statistics
- Machine Learning Algorithms
- Marketing and Retail Analytics
- Data Warehousing
- Web Analytics
- Finance Services Analytics
- HR Analytics
- Text Analytics
- Data Visualization
We were exposed to different tools and technologies like R, Python, SAS, Tableau, Hadoop, Spark, etc. The curriculum was very well designed with concepts and real-life application-based case studies going hand-in-hand.
Campus Life: A Bag Full of Memories
My days at Praxis were full of assignments – tons of quizzes, semester exams, team presentations and, of course, the main group capstone project. The environment at Praxis and the quality and attitude of the faculty was superb. This new world of new subjects and exams turned out to be very interesting and the challenges worth taking.
Additionally, the class was a mix of highly talented professionals from diverse industries (there were a few freshers as well). This encouraged healthy discussions, brainstorming, and learning from each other. In addition to academics, there were various cultural and sports events that lent a good balance between studying and campus life.
The First Analytics Job Offer: Outcome of the Intense Routine
The placement season in Praxis starts as early as November every year. But the preparation for it starts from Day 1 of joining Praxis. I was fortunate enough to get a job offer from the National Payments Corporation Of India (NCPI) in the role of a Data Scientist. This happened through the campus placement program. Three other colleagues were placed in NCPI, while others were placed in organizations across different verticals and function areas.
In NPCI, my team was building a Fraud Risk Management Model to predict and prevent different kinds of fraud transactions – ATM, UPI, POS and E-commerce. We worked on different technologies, such as Python, R, PySpark, Julia, Tableau and Hive.
It was my first experience working with petabytes of data. The NPCI journey, though short, was interesting, challenging and a great learning experience of nearly one and a half years. The classroom training received at Praxis and the rigor that we were made to go through in that one year turned out to be very helpful in not just getting the job but in performing with distinction.
The next BIG move: Learn and grow
It is exciting setting targets and moving forward to achieve them. After a successful professional career in NPCI, I got the opportunity to move to one of the BIG 5: AMAZON.
I joined Amazon as a Data Subject Matter Expert (Data SME) in the Alexa Data Services branch of Amazon Analytics. This was indeed a dream come true and I personally believe that my academic and industry background, my decision to leave my job and enroll into a full-time program in analytics at Praxis, and my stint with NCPI were all contributors to this achievement.
My current role involves working with Alexa Machine Learning Scientists to enhance the Alexa experience. I am quite new to this venture and am committed to making this a successful one.
It is my utmost pleasure to share my story on the portal which has an equal contribution in building my Data Science career as far as self-study through blogs and competitive hackathons are concerned.
As artificial intelligence and machine learning are grabbing the world with their presence, we also need to learn new things, take risks, work a little harder and start looking for opportunities.
If you are serious about a career in Data Science, take time off and register for a full-time program, – and use that time to dive deep into the domain. It is quite complex and will take a lot of time and work – but the results are worth the effort.
Data Science, AI and ML – these are great opportunities – if you like technology and numbers, go for it – and as they say at Praxis – ‘CELEBRATE YOUR WORTH’.
About the Author
Ankita Ghoshal – Data SME at Amazon Alexa Data Services
I have more than 6 years of work experience in the area of Web Application Development and Machine Learning, including Fraud Risk Management, Anti-Money Laundering, E-commerce and other domains.
I graduated from ITER, Bhubaneswar in IT and later pursued PGP in Data Science from Praxis Business School, Kolkata. I started my professional career with TCS and switched to NPCI through Praxis Campus placement. After working with NPCI for 1.5 years, I transitioned to Amazon India where I am currently working. I enjoy going through AV articles/blogs on new technologies and ML algorithms and like spending my leisure time painting and cooking.
This is a sponsored post and the opinions expressed in this article are exclusively of the author. A few minor edits have been made by Analytics Vidhya.You can also read this article on Analytics Vidhya's Android APP