What was it like to take Computing for Data Analysis (CSE 6040) course in Fall 2017?

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Computing for Data Analysis is a data science introductory course from Georgia Tech at edX, part of the MicroMaster in Analytics worth 9 credits accountable for the Online Master of Science in Analytics from the same university.

This course teaches you Python from the very beginning, in fact it was not a difficult course, but don’t be fooled by that. It was work intensive indeed. Let me share with you a progress graph that shows the quantity of assignments I had to do to finish the course.

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If you are an edX student like me, you can see this graph clicking in the “Progress” tab of the courses you’ve taken. As you can see, there were 15 notebooks this Fall 2017, 16 if you count the Midterm 1-R. There were 2 Midterms and 1 Final Exam.

This is how grade was divided:

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As shown, there were so many notebooks, but they only accounted for 50% of the grade, 3 exams were 50% of the grade.

Just as a side note, you can find me through my Linkedin profile and ask me any question related to OMSCS, OMSA or online education in general. Just send me an invitation and I’ll be glad to share my network with you.

I liked this course, because it taught me things that I think are very basic every day usage concepts that I hadn’t had the chance to polish on. I’ll share with you the list of topics:

  1. Python Essentials (there were some things I didn’t know of, like functional programming)
  2. Pairwise association mining
  3. Math prerequisites review (a lot of linear algebra and matrices stuff)
  4. Representing numbers
  5. Preprocessing unstructured text (always so necessary)
  6. Mining the web (useful indeed)
  7. Tidying data
  8. Visualizing data and results
  9. Relational data
  10. Intro to Numpy/Scipy for numerical computation (I had already gone through this, but a little brushing up wasn’t bad)
  11. Ranking relational objects
  12. Linear Regression (a little Machine Learning to make things more interesting)
  13. Classification (more ML)
  14. Clustering via k-means (still more ML)
  15. Compression via PCA (ML, ML, ML)
  16. Eigenfaces (personally I hate this topic, but very recurrent in Analytics courses indeed)

You can take a look at my course certificate if you want. That’s how it would look yours if you take the course or any other edX course.

Pros:

  1. I liked the course, it touched very foundational topics and my favorite language after php and javascript: python, what an important and fun language to learn.
  2. I liked how we used Jupyter notebooks, I hadn’t had the chance to work with them. Vocareum is a web jupyter notebook server I didn’t know, a real jewel.
  3. I liked the level of automation in this course, almost fully automated. We could instantly know what our grades were after every response we gave, all thanks to Vocareum.

Cons:

  1. From my point of view, there was relatively short time to address midterm 1, which was in Vocareum, there was a lot of reading to do to understand what to do on each question. I have nothing against to coding as part of an exam, even though it was the first time for me in this way of doing things, but time was an issue, at least for me.
  2. Some topics like “Representing numbers” were a little boring to me.
  3. I took this course as a verified student, there were other 2 types of students: audit students and Georgia Tech students that took the course as part of the Master of Science in Analytics. GT and verified student forums were put in Piazza (a very handy forum software used by other famous courses) in 2 different groups, with majority of students coming from GT, so verified students were kind of isolated from the majority of the perspectives on the course topics, which made learning not as fun as other online courses I’ve taken.

In sum, this course took a lot of time from my schedule in the 15 weeks it lasted, from 5 to 10 hours per week maybe. Not as much as other courses, but you already saw the number of assignments that were allocated. If I had the chance to take it again, I would do it, it was fun and it strengthened basic foundations for me as a data analyst. I felt I had to tune my python knowledge a bit, and this course did just that. Hope you like it as much as I did if you are about to take it. Best of luck! Don’t forget to connect with me in LinkedIn, visit my public profile. I’ll be glad to share my network with you. See ya.

What was it like to take Computing for Data Analysis (CSE 6040) course in Fall 2017?

5 thoughts on “What was it like to take Computing for Data Analysis (CSE 6040) course in Fall 2017?

  1. Abi Shek says:

    I connected with you in Linkedin, looking for some notes and help, but found unresponsive. Could you please share some notes and explain a little more on how exams are conducted. Thanks

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  2. Exams were Vocareum assignments for which we were given 24 hours to submit. No proctoring, just a timer. First of 3 exams lasted 2 hours long, but based on our discomfort based on amount of work to finish in 2 hours teacher changed the exam format to what I just told you about. I guess he might change it for this semester though. Still professor in very responsive and he dealt very well with the situation

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