Last month, I learned that the official Chicago Python user group, ChiPy, organizes a mentorship program for members looking to improve their Python skills. I applied to the Data Science track and was assigned to Eric Meschke, who is a software engineer working in the finance industry. I met up with Eric a couple of weeks ago, as well as his mentee from last year, Alex Flyax. It turned out that Alex, like me, had finished his postdoc and decided to transition to a career in data science. By the end of the mentorship program, he had successfully obtained a job at a startup in Chicago, where he is currently working. (So basically, I hope to follow in his footsteps.)
I'm really excited to have the benefit of both Eric and Alex's perspectives. At our first meeting, they shared very good advice on what it's like to work in the data science field as well as tips on preparing job applications. Afterwards, Alex emailed me a long list of resources, particularly textbooks and videos that would be helpful in shoring up my theoretical knowledge.
When I applied to the program, I stated that my goal was to complete a Kaggle competition to use as a portfolio project during the job hunt. That turned out to be a good idea, since it was exactly what Eric and Alex did last year! Eric advised picking a competition that has clear metrics for evaluating submissions, and Alex recommended sticking to general machine learning methods (rather than more specialized methods for time series data or natural language processing) for now. I looked over the open Kaggle competitions and decided to tackle the one on San Francisco crime classification. As a former resident of the Bay Area, it's a topic that I find personally interesting. I may end up working on this project together with Alex's mentee, Zaynaib (you can go read her blog posts on ChiPy as well).
I've worked through the Kaggle tutorial on Titanic survival data at Dataquest. (I've put up my iPython/Jupyter notebook going through the steps at Github: titanic.) I've also been reviewing probability and statistical theory and learning more about machine learning methods through watching course lectures.
By the end of this mentorship, I hope to have accomplished the following:
- Make a submission to the SF crimes Kaggle competition that scores in the top 10%, using only Python.
- Study statistical theory, machine learning, and algorithms.
- Do at least one practice job interview with my mentors.
- Apply to data science jobs (...and hopefully get interviews and an offer letter).