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How I started with Data Science (in 2012) ?


As of today, I have around 15K+ followers on LinkedIn, and at least 10 connections in a day ask me how they can start or build their career in data science. As I see it, the problem is not that there is a dearth of resources but the problem is that there are plethora of resources (MOOCs, Books, YouTube, Blogs, Paid Certificates etc) out there and a newbie gets overwhelmed.

While a particular path chosen can’t be absolutely right or completely wrong, I believe one should choose resources based on his learning & adaptability preferences to optimize his learning path. I have written a couple of posts in the past for newbies on how to learn data science; today, let me share my journey with the starters/newbies to give them an idea how it is like to get into data science.

I started my career in 2005 working with Oracle (SQL & PL/SQL), over the period of time, I got to work on other operational databases (SQL Server, MySQL etc) along with designing DWH building ETL pipelines and building BI dashboards on top of DWH.

In 2012, I read the famous article by Thomas H. Davenport & D. J. Patil in Harvard Business Review titled as Data Scientist: The Sexiest Job of the 21st Century. As I was working with databases & data warehouses, I got curious what this data scientist is and how his work would be different than mine. While digging for data science, I came across Machine Learning course by Andrew Ng on Coursera. I attended that course and while I was able to learn most of the theory out of it, I was looking for some hands-on, then I got introduced to Kaggle and the first problem I worked was Titanic: Machine Learning from Disaster.

By 2013, I was in a position to suggest my employer to start with a PoC on data science in our department. As it was a very new field that time, I had a hard time to convince people why should they invest time & resources in it. In the mean time, I kept working on Kaggle competitions, public data-sets, learning statistics/probability, keeping a tab on emerging trends.

Finally in 2014, I got the chance to work upon a use-case, where I was able to convince the stakeholders how data science is different from traditional analytics. As I had a background of data design/architecture, initially I got data science use-cases as an added responsibility while working on traditional IT projects. But once I got started with that, there was no looking back.

Since then, I have worked on many interesting problems and because of my background, I have also built many data science platforms from scratch to enable the team of data scientists either for our customers or our business. In my current role, I work with a team of data engineers & data scientists to build & enrich data science platforms.

While working on data science use-cases and projects/products, I got to learn many things; on one hand, I learnt how data quality (and hence data governance) is important for the success of data science projects/products; on the other hand I got exposure to hypothesis testing on sample data during the PoCs to ascertain whether the use case can be progressed as a data science project or product.

This is my journey till where I stand today, as its an ever evolving field, there are gaps between theoretical aspects and real-world problems we are trying to solve. One very important thing that I have learnt that it is a team sport. An individual can not have all the skills required to work on a data science project so we need to have diversified skill-sets within the team, from business/domain, from math (statistics/probability) & from software/data engineering. We meet new challenges almost everyday (technical as well as non-technical), and we keep learning, discussing, trying till those are resolved.

So what can you learn from my journey? Keep exploring, keep learning, keep working on some interesting problems/projects, keep interacting with people in the same field, learn from experienced people, guide the starters/newbies based on what you have learnt till now.

If you want to know what topics that you need to work on while building your data science skills, please refer my post Data Science Digest.

Thank you for reading my post. I regularly write about Data & Technology on LinkedIn & Medium. If you would like to read my future posts then simply ‘Connect’ or ‘Follow’. Also feel free to connect on Slideshare.

More where this came from

This story is published in Noteworthy, where thousands come every day to learn about the people & ideas shaping the products we love.

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#DataScience #MachineLearning #Coursera #Newbie #Kaggle

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