Are you a self-made data scientist? How did YOU do it? by Sudalai Rajkumar S
Answer by Sudalai Rajkumar S:
Here is one question which I think am qualified to answer for.!
I am a Mechanical Engineering graduate who had no prior knowledge about data science or for that matter even coding when I left my college six years back. Now I am working as a Lead Data Scientist in a reputed firm and also one of thein the world.
Though I do not have formal background in CS or Statistics or Maths, I have a passion for crunching numbers and finding patterns right from my school days. I think anyone with a good passion for patterns and numbers coupled with right amount of hard work can become a self-made data scientist. Here is my path :
This played a major role and is the first place in my learning path. Courses which helped me understand the basics concepts are
- by Edx – This is very good introductory course in Statistics which taught me the basic concepts
- course in Coursera – A very famous course by Andrew N G which most people are aware of
- course in Edx – This is again a very good course with a lot of practical examples
- by Standford Online – This is again a very good course by which teaches the concepts of predictive modeling in detail with R codes. The curriculum of the course closely follows this
Some other nice online courses which I came across are
- by Harvard Extension – This is a very good course for people wanting to learn the concepts using python.
- by Edx – This is a very useful course for people starting with big data analytics
- by CalTech – This covers the basic concepts of machine learning
- by Coursera – Interested in knowing about the new boy (Deep Learning) in town. This course is the perfect place for that taught by none other than Geoff Hinton himself.
Once I get a fair understanding of the DS concepts from these courses, I was itching to use them somewhere. I was looking for options to test these theoretical skills. That is when I came across DS / ML competitions.
DS / ML Competitions:
I came to know aboutwhen I was searching for datasets to apply my learnings. I thought that I can ace the competitions easily since I have a good understanding of basic concepts. Poor me was not aware that hands-on is a different ball game from theory.
I started doing competitions on Kaggle but ended up at the bottom half of the table inspite of all the hard works. So once the competitions were over, I started looking at how others solved the problems fromand . This is one important place where most of my learning took / taking place.
It also helped me hone my structured thinking on approaching the DS problems. It also helped me work on different real world datasets from different domains, each one challenging in its own way. When working deeper on these problems, I got new learnings every time and helped me improve myself further.
Doing Kaggle competitions at the first go might be daunting these days since the competition levels are quite high. So one can try to work on data science problems in other platforms like, , etc before trying out on Kaggle to gain some confidence.
Apart from MOOCs and DS competitions, two important sources that helped me with my learning and understanding of this space are
I follow these two blogs to update my knowledge and to keep up myself to the advancements in the field.
Other resources which I found to be helpful are
- (To understand the happenings in India)
Hope this helps other budding self-made data scientists.!