musings on music and life

August 14, 2016

Microsoft DAT210x

Filed under: Data Science, education — sankirnam @ 12:15 pm

I recently completed the course Microsoft DAT210x: Programming with Python for Data Science on Edx, so I’ll just take a moment to review it here. I’m still taking the Data Science bootcamp by Logit, and took this as a supplement to get additional exercises with Python, Pandas, Scikit-Learn, and other Data Science-related packages.

The course was just introduced by Microsoft last month as part of their “Online Data Science Degree Program“. As such, I took the course from July-August, and this was the first iteration (the course just ended last Friday). That being said, 6 weeks is not much time to teach something as broad as “Data Science”. The course starts with an introduction to the subjects of Data Science and Machine Learning, and then progresses into an introduction to Pandas, which is a Python package for the manipulation of DataFrames, similar to what you do in R. After also covering a brief survey of 2-D and 3-D visualizations with Pandas and matplotlib, the course then covers data transformations and dimensionality reduction, namely PCA and Isomap (for non-linear dimensionality reduction). After that, the course covers several important algorithms used in supervised and unsupervised Machine Learning, including K-means clustering, K-Nearest Neighbors classification, Ordinary Linear Regression and Multiple Linear Regression, Support Vector Machines, Decision Trees, Random Forest Classifiers, and a final rush through confusion matrices, cross-validation, pipelining, and tuning parameters with GridSearchCV.

Given that this was the first iteration of the course, my experience was pretty good. The course could use a little more polish, both in the presentation of the online materials, quizzes, and programming assignments. There were minor typos all over the place (though they didn’t really impede understanding of the material), and the quiz questions were rather ambiguous from time to time, much to my consternation. The explanations of the concepts themselves were a little wishy-washy, but when you’re trying to address a general audience there’s little else you can do. Links to the literature, textbooks, and further explanations are included, and should be read as well in order to gain a complete understanding of the subject matter.

The programming assignments were great, however. They were quite challenging, and I think I spent more than the recommended 4-8 hrs/week on the assignments. They were all really interesting, and challenging enough where I didn’t get completely frustrated and give up. We used PCA and Isomap to project 3-D images into 2-D space, K-Means to identify people’s residences based on anonymized geolocation data from their cellphones (!), linear regression to reconstruct audio samples (sort of like what they do on TV shows!), SVC to analyze whether or not someone has Parkinson’s based on collected speech quality, and other interesting examples.

I would recommend people taking this course do as I did, and use it as a supplement for other courses. There’s no way you can learn everything there is to learn about a subject as broad as “Data Science” from one course, and it is good to take multiple courses because some of them explain certain concepts better than others. For example, this course covers Isomap, which is something that most other courses do not.

If you’re curious about the programming assignments, I’m posting them in my github.

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5 Comments »

  1. Hi,
    I found your blog while doing research on Logit Data Science and Codesmith. I have a Systems/technical background but thinking of getting into data science (or full stack web development) I’m wondering how has your experience with Logit so far? The cost is far less than Codesmith, but it is the first cohort and there is no job/hiring data. Well, to be honest, there is not a lot of jobs/hiring data on Data Science bootcamps. Any thought much appreciated!

    Thanks!
    Amy

    Comment by Amelie — August 30, 2016 @ 6:01 pm

    • Hi! The experience so far at Logit has overall been positive. It was very tough, but that’s what we all signed up for. It was very difficult, more so for me than others, especially since I do not have *that* strong a programming background relative to others. This actually was the first time in my life taking any class related to programming/CS!
      If you are interested in Data Science, I would recommend brushing up on your math (Linear Algebra and mutivariable calculus) and vanilla Python as much as you can. Doing some online courses on Data Science and Machine Learning (e.g. those found on Coursera, Udacity, or EdX) would also be a good idea, so you are not completely overwhelmed due to seeing the material for the first time ever! For me at least, the more times I see the material, the more sense it makes, and that’s why I’ve found taking all these online courses useful as supplements.
      I unfortunately don’t have any information on hiring. We met with a resume reviewer this week, and are scheduled to meet with a recruiter soon. Some of the people in the class have started applying to positions on their own, and the rate of callbacks for interviews seems to largely be a function of prior experience and how strong the other parts of their resume are.

      Comment by sankirnam — August 30, 2016 @ 10:06 pm

      • Thank you so much for the Info, I really appreciate do it. If you have any more observations, (positve or negative) that you’d like to share, I’m ALL ears!

        Comment by Amy — September 1, 2016 @ 7:38 pm

  2. Thank you very much for the information. I’m about to enroll in my first data science class, which I hope will help me study bioinformatics (post-graduate degree).

    Comment by Narasimha — October 4, 2016 @ 5:19 am

  3. […] DAT210x on EdX is also highly recommended, and I also wrote about it after completing the course. This course gives plenty of practice with machine learning, and will […]

    Pingback by My experiences with learning “Data Science” in 2016 | musings on music and life — December 16, 2016 @ 11:21 pm


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