My attempts with online learning resources.
Inspired by Tirthajyoti Sarkar’s post on choosing effective courses for machine learning and data science, I’m going to take stock of my progress so far and make an effort to figure out how I can better use the available educational resources.
Pre-2018 Relevant Courses
On evaluating the classes that I’ve taken, out of 21 audited MOOCs, I wouldn’t classify any of the contents covered at beyond a basic level of comprehension. This is my subjective view of course, but it gets at one of the biggest issues that I have with MOOCs: I can’t seem to move forward. MOOCs have been great for me to understand a breadth of material but I haven’t been able to get deeper into the subjects that interest me.
- (Data Science | Basic) Data Visualization and Communication with Tableau, Duke University, Coursera
- (Data Science | Basic) The Data Scientist’s Toolbox, Johns Hopkins University, Coursera
- (Data Science | Basic) Getting and Cleaning Data, Johns Hopkins University, Coursera
- (Data Science | Basic) Regression Models, Johns Hopkins University, Coursera
- (Data Science | Basic) A Crash Course in Data Science, Johns Hopkins University, Coursera
- (Data Science | Basic) Developing Data Products, Johns Hopkins University, Coursera
- (Data Science | Basic) Building a Data Science Team, Johns Hopkins University, Coursera
- (Data Science | Basic) Data Science in Real Life, Johns Hopkins University, Coursera
- (Data Science | Basic) Managing Data Analysis, Johns Hopkins University, Coursera
- (Data Science | Basic) Computing for Data Analysis, Johns Hopkins University, Coursera
- (Data Science | Basic) Introduction to R for Data Science, Microsoft, edX
- (Data Science | Basic+) Introduction to Recommender Systems, University of Minnesota, Coursera
- (Data Science | Basic+) Practical Predictive Analytics: Models and Methods, University of Washington, Coursera
- (Finance | Basic) Competitive Strategy, Ludwig-Maximilians-Universität München, Coursera
- (Finance | Basic) Analyzing Global Trends for Business and Society, University of Pennsylvania Wharton School of the University of Pennsylvania, edX
- (Programming (py) | Basic) An Introduction to Interactive Programming in Python (Part 1), Rice University, Coursera
- (Programming (py) | Basic) Programming for Everybody (Python), University of Michigan, Coursera
- (Programming (Web) | Basic) HTML5 Coding Essentials and Best Practices, World Wide Web Consortium (W3C), edX
After making this list (which took a while), I can see that at least part of the cause of my problem is that I keep picking basic courses to take. Reflecting upon how I picked these courses, I think the sequence of events unfolded something like the below:
- The MOOC platforms started to offer Specializations
- I did not keep track of which Specialization the course I took belonged to or the future classes in the Specialization
- When I finished a course and searched for another class on my topic of interest, I came upon more Specializations
- I looked at the syllabus for the first course and there would always be at least one item that I didn’t feel like I had a good grasp of
- The result is that I keep taking the first few courses in multiple Specializations
It’s surprisingly hard to keep track of courses when only auditing (this is probably intentional). Also, the lack of structure in the self-paced classes hasn’t worked so well for me either. But since I’m getting all of this top rated education available for no cost, I can’t complain and it is really on me to keep track of my learning.
At this point now with so many people sharing their own experience of self learning data science, it shouldn’t be too hard to come up with a curriculum as a student. Colleges also publish their data science curriculums, and using that as a basis might be a good idea.
Another point that I should consider is that most of the classes I’ve taken have been through Coursera, and Coursera restricting the quizzes and assignments has been detrimental to my learning. I should probably explore more non-Coursera options too.
So this is my big takeaway: keep track of courses and specializations and create a curriculum of courses that can build upon one another.