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Georgia Institute of Technology

Computing for Data Analysis

Georgia Institute of Technology via edX

Overview

The modern data analysis pipeline involves collection, preprocessing, storage, analysis, and interactive visualization of data.

The goal of this course, part of the Analytics: Essential Tools and Methods MicroMasters program, is for you to learn how to build these components and connect them using modern tools and techniques.

In the course, you’ll see how computing and mathematics come together. For instance, “under the hood” of modern data analysis lies numerical linear algebra, numerical optimization, and elementary data processing algorithms and data structures. Together, they form the foundations of numerical and data-intensive computing.

The hands-on component of this course will develop your proficiency with modern analytical tools. You will learn how to mash up Python, R, and SQL through Jupyter notebooks, among other tools. Furthermore, you will apply these tools to a variety of real-world datasets, thereby strengthening your ability to translate principles into practice.

Taught by

Richard W. Vuduc

Reviews

2.5 rating, based on 6 Class Central reviews

4.2 rating at edX based on 10 ratings

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  • Anonymous
    This could have been a very meaningful course, however, the key issues here were: 1. Content delivered (i.e. lectures and homework assignments) did not reflect the content tested. That is, the content tested was very different from the content deli…
  • Anonymous
    Overall, I think the content of the course is very good. However, the execution is beyond terrible. There is really no reason this should be an "EdX" course, as they do not use any EdX technology for the course. Not the discussion boards, not the assignments. I guess you can count the videos. The videos need work. There may be 10-15 total minutes of videos for each topic that do not even teach anything, they basically go over what the assignments will be. After taking some other classes on Edx that I felt were really really great, I am pretty disappointed with this class. If I wanted to teach myself everything about the topic, I would not have paid for an EdX course.
  • Anonymous
    For the most part, this was an excellent course. The lectures were well done, and I learned some valuable information, not just about the algorithms, but also about efficiency and accuracy. A good portion of the course involved using Vocareum (hos…
  • Anonymous
    This is a review of the fall 2018 session. This course offers very high quality jupyter notebooks, and interesting assignment problems. The notebooks are almost exactly matching https://github.com/cse6040/labs-fa17. Both Verified and Audit students…
  • Anonymous
    This course is hands-on (aka teach yourself!). The teaching methodology is min to no lectures and throwing you notebooks for you to teach yourself. I understand you must come with basic Python experience, but after a certain point, I expect you to teach me.

    The notebooks are written in the most convoluted and unclear way possible. Spent most of my time reading the 3 paragraph question, trying to decipher what was even being asked. Why? What's the point? Are you unable to ask clearly? Are you trying to show off? Are you trying to confuse people?
  • Spalthoff Daniel
    I dropped out of this course after four weeks, for personal reasons (travel, work). The course was very demanding, but interesting. However, course support was not sufficient. Recitations with tutors were offered, but suitable only for American time zones. Although a second recitation time slot was announced, this was not accessible for edX students. As I found the course very interesting, I will probably give it another try in the future; but I hope the support for edX students will be better.

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