Syllabus Day
For some of you, this title may have brought back painful memories of sitting in Biology 101 your freshman year of college and listening to the professor drone on and on about the semester ahead. Lucky for you, I won’t be doing that; however, I am going to lay out my schedule for the year and discuss why I made certain choices.
Assembling the Schedule
Once I decided that I was going to embark on this “Data Science Odyssey,” I knew I was going to need a guide. Personally, I work best when I have something that tells me to do A on day X and do B on day Y. I know that life will inevitably get in the way; nevertheless, a schedule is a great way to hold myself accountable. To make this schedule, I started by looking at the Data Science programs from the following institutions: Stanford, University of Washington, Cal Berkeley, Georgia Tech (Masters of Analytics), Carnegie Mellon (MS in Machine Learning), and Cornell. Each of these schools had some sort of Data Science program that was either run by their Statistics or Computer Science Department. I have compiled the courses associated with each program and their respective descriptions here. After looking at each of those programs, I have decided to separate my schedule into four phases: Review, Foundations, Application, and Capstone.
Review
While most of the Python libraries and R packages that anyone will use out in the “wild” handle most of the math, I want to understand what’s going on under the hood. Having an understanding of the underlying statistics and calculus that is being used will do nothing but help me down the line. While I’ve taken courses in these subjects in the past, I think it will be good to do a thorough review.
January
- January 9 through January 19: Active Calculus and Khan Academy (Mulitvariable Calculus and Differential Equations)
- January 20 through February 4: Linear Algebra
February and March
Foundations
After reviewing integrals and continuous distributions, the next thing to do is to set a solid foundation. I’ve decided to allocate only two months for this part so that more time can be spent exploring topics in the application phase and working on my pseudo-capstone project. I opted for an introductory machine learning course and intermediate statistics course. The reason I chose a machine learning course is very simple: I don’t have any ML experience. While I know most solutions won’t involve ML, I think having a good understanding of the concepts will prove to be invaluable. When it comes to the statistics course, you’re probably thinking this guy must really enjoy math. While that is true, one of my regrets is that I didn’t get a solid stats background while in undergrad or during my first year of graduate school. So, this is my opportunity to right that wrong.
April
May
Application and Fun
The first five months of this journey are all about understanding the basics. The application phase is all about seeing what I can do with what I’ve learned so far and having a little fun while I’m at it. The topics that will be covered over the next four months will help me gain a better grasp on things that I’m interested in such as recommender systems and sentiment analysis.
June
July
August
September
- Data Science in the Cloud: I’m going to give myself a month to play around with some of the available cloud technologies that make doing machine learning and intensive data analysis easy. If there’s a book that is released between now and September, I might go through that as well.
Capstone
You can’t say that you’ve truly mastered something unless you create some type of original work. In most academic programs, that is usually done in a thesis, capstone project, or dissertation. I don’t really like the idea of writing a 40+ page paper so I’m going to do a capstone project. This project will focus on applying everything I’ve learned thus far.
I’m gonna take the last three months of the year and see what I can come up with.
Getting it Done
Many of the links above are associated with books or academic courses. With the courses, I plan to tackle the homework assignments and projects; when it comes to the books, I will do my best to solve the problems and complete the activities that are provided.
While I won’t have any type of certification or degree at the end of this year, I believe that I will retain the things I’m spending the time trying to learn and that’s more important than any piece of paper.
I’ll be checking in every week to report on my progress and what I’ve learned so stay tuned!
