Finally, the day has come and I graduated from Flatiron’s part time Data Science program. And it has come and gone. Now I am left with a very tough question, how do I retain and keep up to date on all the skills I learned. I have asked my instructor and he recommended some HackerRank daily practice problems along with code wars. I didn’t really like these ideas as I didn’t really like the structure and set up of these. Being the genius instructor he is, Jeff recommended doing another project. A project that I choose and feel very passionate about. I never really thought about it until now.
I have finally made it! The end of my Flatiron education. To finish it all off a capstone project related to two of my favorite things, the WSJ and the economy. I am very proud of my capstone and believe it encompasses a multitude of the python capabilities I possess. This last project cemented my Python understanding. Now, for all of the details surrounding my capstone project and what impactful lessons I learned.
Project Background: The following Jupyter Notebook uses different classification machine learning modeling techniques to try and correctly identify a NBA players position based off of their physical traits and game stats. Due to the NBA shifting into a position-less basketball style of play, I thought it would be interesting to apply statistical python code to solve this problem.
What are the best zip codes to invest in? Answering such an ambiguous question was the biggest challenge of the project. I do have quite the finance background, so I immediately thought of a couple of options to answer this question. The different measurements I came up with were ROI, historical average price, standard deviation of the historical prices, current market value, and weighted average return.
The module two project was a huge step in my Data Science career. It cemented the statistical nalysis techniques we learned in Module 1 and 2. In addition, I furthered my understanding of SQL and hypothesis testing. All in all, I am extremely happy with the outcome of my Module 2 project and cannot wait to continue my Data Science journey.