Why I decided to go with Data Science

Russell Comer
Oct 29, 2017 · 3 min read

Data Science

I have recently decided after long deliberation to move forward with data science and leave behind web development behind. Not so much leaving it behind but more like no longer focusing on it. I have tried just about every type of programming language and web development practice you can think of. From Android programming to using node.js with full stack javascript development. There is nothing wrong with doing any of that, but for me personally I don’t see a future there for me. It might be something that I revisit one day but it’s no in the plans anytime soon.


My focus is going to be Data Science with python. Why? Well, I started learning python a few years back and really liked the language and second because I feel I can make more of an impact for people using data science than I can with building web and mobile apps.

There is so much data out there that has yet to be analyzed and web development seems to be so saturated. I heard a stat not sure how accurate it is but they said that about only 15% of NASA’s data is analyzed. Now granted they take a ton of data that may or may not be necessary but still it remains there is a lot left on the table and not just by NASA but in every industry.

Why Python

I chose Python because it’s one of the most versatile languages out there, it can be used for just about everything. That way if I move back to web development there will be django or flask. Might even use them to build some type of data driven product who knows. Point is python is pretty awesome.

I really enjoy astronomy and occasionally doing some astrophotography and python can be used for that in fact its widely used in astronomy and is used by NASA for a lot of their data analysis.

Data Is Everywhere

The fact is data will always being generated, there will always be a need for making data driven decisions. Whether that’s for business or discovery of something important it will always be there.

Data is everywhere, and the fact that it’s everywhere can be useful towards real world problems. Like big problems such as our economy or small problems like figuring out whats really effective on making your phone battery last longer.

Financial Data

The biggest draw I have to data science besides studying space is financial analysis of the stock market. I’m not so much interested in high frequency trading as just using an algorithmic way to do stock analysis. I want to see correlation of stock prices at certain times with what macro event happened that during that period and build predictive models based on that data to predict stock prices. Based on that data it will tell me how certain stocks and sectors trade due to certain events. I know some events don’t repeat themselves but it’s more or less finding a pattern as to why that particular event would have affected the decision to buy or sell a stock. If two totally different events happen but the result of those events render the same affect on a stock then I know to be on the look out for events that would take the same type of effect.

I’ve always like working with data and making different calculations on that data but all from inside of an excel spreadsheet. Granted some of what I’m going to do can be done inside of a spreadsheet but it’s more the point of wanting to code and do it more from scratch rather be limited to that of a spreadsheet. Spread sheets struggle with big data which is where python really boasts it’s power and the whole idea of that is fascinating to me.

Originally published at Russell Comer.

Russell Comer

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Builder of Software. Trader. Data Science Enthusiast.