27th November 2019 - Raviteja Gullapalli* * Disclaimer: Not another suspense thriller. Also, no Jupyter notebooks yet.
Here’s my first post on data science, quite different from the rest of the content on this blog. Buckle up, because you are going to see a lot of similar posts coming up on this topic in future.
So, What does a CAE analyst has got to do with Data and Python? Before that let me introduce myself. Working for the automobile industry has been my life’s aspiration. Having worked for almost 5 years in the mobility sector, it is fascinating to see how the Industry 4.0 is shifting gears and steering disruption powered by data science. The vast scope of data and technology driven approaches towards innovation and problem solving inspired me to be an inventor and take up data science as my ultimate career goal. By building a strong foundation of data science and by applying skills mastered as a crash test data analyst my aim is to solve complex real world business-and-social problems, to keep up with the emerging technology innovations and to seek a teaching and research position in a longer run.
Data Science. What is it?
According to a recent study by mcKinsey / HBS blah blah…I’m not gonna emphasize on all that, by now, everyone knows what data science* is and how important data is for business and innovation. But, as a math enthusiast and a programmer at heart (Sure, as an automotive enthusiast too) what is it? Here’s my simplest definition. * *Data science is a field of science that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data, a science that’s fun to learn and easy to apply.
So, Until now I’ve learnt data science in bits and pieces, between those *important *hours of sleep and work. Luckily I found the Coursera’s IBM professional certificate the cheapest and the best *organised *for my requirements. Also Andrew NG’s Machine Learning course. (Not endorsing anybody, you can do your research before taking up any course on Data Science, there are tons of good courses out there online.)
After months of self learning, I felt the urge to share a few zero day tips to excel in the field of data. Every day is a zero-day with data.
Tips and conclusion:
- When it comes to data science, nobody highlights the fact that ‘87% of data science projects never make it into production’ – another fact from Gartner again. So even though you have the right data available, data science may not be the perfect solution to solve your problem. Use the time wisely.- Use the python and DS cheat sheets, so that you do not spend a lot of time on google!- Make use of the network, do not be a lone wolf. There is a lot you can learn from others.- Understand the mathematics, being a CAE analyst I can easily relate to the statistical concepts behind ML. Also domain knowledge helps.- Don’t be beaten up just because of your silly mistakes that lead to failure. Nobody is perfect. Just practice more and more and try not repeating the same mistakes again. Thanks for reading. Peace. RT.