I have to say that I have really enjoyed learning git. Every project I do, I learn to appreciate it a bit more. Part of my interest is for a completely oddball reason - I like using terminal since it fits well in my workflow. I can have a prototype of an idea created that shows basic functionality of the initial goals. This is my proof of concept.

Even though I have spent a significant amount of time over the past two years learning and teaching data science concepts, I find that I am often using other skillsets to solve problems. As engineer, many of the tasks involve optimizing processes and predicting events from a theoretical perspective. Machine learning is very helpful for repetitive tasks or determining human behavior since data exists where this information can be revealed but finding data of optimized processes or rare events is very sparse. My go to skillset is often simulations, algorithms, and linear programming.

Regression is not a topic that I originally spent much time thinking about but as I started to get more involved with analyzing large dataset, I found myself learning more about regression so I coud extract the most out of a dataset. I have noticed one thing as I have worked with data scientiests from different backgrounds.

SQL is a definite requirement for anyone who is interested in doing data work. Here are some basics from work I have done:

Preprocessing might be one of the less appreciated steps in data science. How much time it takes to do is not clear but in general it is one of the lengthier steps in the data preparation process. Part of the ambiguity is associated with what encompasses data preparation. From my experience these are some of the keys steps:

My last article was a bit long so hopefully this one is more concise. Although this article is an important topic - it’s about personal growth. Everything from this past year where I think I have expanded my current skills or identified a potential preference that brings joy to myself. Below is a short list of some of my key findings:

By September 2022, I had begun distributing my resume to potential employers. It was somewhat of an odd experience - partially because I had not truly done an interview since 2018 and partially because everything is now performed online. I had sent out resumes in February 2020, as I was finishing my data science certificate program at Northwestern, but everything was shutting down due to COVID by the next month and all hiring just stopped. I think I had applied for 20 positions at that time. This time I applied for only 8 positions but each one was something that I thought would be a very good fit and happily had 4 positive responses.

I must say that I have benefitted from the work from home experiences of the past few years. By saving two hours each day on commuting and being able to utilize my time at home very effectively, I have been able to add significant time developing data skills. With my schedule from before the pandemic, I don’t know if I could have had enough time or energy to be able to include meaningful learning. So I give strong praise for work at home for its ability to support independent research.

So I would say that I have been developing my data science knowledge-base since the summer of 2019. Around that time, I took several Standford University online classes and started a six month Northwestern University Data Science Certificate Program and since Spring 2020 I have been a data science teaching assistant.

I made some changes in early 2022 after realizing in 2021 that my schedule was just not productive with my future interests. By mid-2021, I had completed my data science certificate program at Northwestern University, two rotations as a teaching assistant with the same program, 4 data science online classes, and multiple self-study projects.