After a couple semesters in Georgia Tech’s OMSCS program, I returned to independent study of data science.
I recently heard about the Summer of Data Science that Renee M. P. Teate organized.
As she describes it:
The Summer of Data Science is a commitment to learn something this summer to enhance your data science skills, and to share what you learned.
How to participate in the Summer of Data Science:
1) Pick a thing or a short list of things related to data science that you want to learn more about this summer.
2) Make a plan to learn it (like an online course, a practice project, etc.).
3) Share that plan on social media, then post updates as you make progress, with the hashtag #SoDS17.
I figure since I already committed to myself to spend the summer studying data science, it makes sense to share what I work on and how I go about the process.
For any #SoDS17 people reading along, I’ll share a little about my relevant background.
1) Several years of experience doing data analysis using tools like Excel and SQL.
2) Programming background that includes an undergraduate degree in computer science from Villanova University, plus several graduate-level classes at Villanova and Georgia Tech.
3) Freelance web development since 2011, consisting of mostly custom WordPress implementations.
4) Work as a basketball scout and consultant, blending conventional player evaluation techniques with aforementioned rudimentary data analysis.
5) Started working with Python in 2015 and R in 2017.
Because I really enjoy working with R, I decided to make it my primary statistical language for right now.
My plan as it relates to the Summer of Data Science ‘17 looks like:
1) Complete the Data Scientist career track on DataCamp. (15 of 23 courses complete)
2) Review linear algebra.
3) Start taking Andrew Ng’s Machine Learning course at coursera.
Most of my data analysis work up to this point centered around doing exploratory data analysis and using basic tools and statistics to make inferences.
So far on my R journey I focused on building my skills at importing, cleaning, and manipulating data; conducting exploratory analysis; and building visualizations.
I’d like to reach a point after this summer where I feel comfortable starting to build some models. From there I expect to dive more deeply into studying machine learning.