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Harvard University

Using Python for Research

Harvard University via edX


This course bridges the gap between introductory and advanced courses in Python. While there are many excellent introductory Python courses available, most typically do not go deep enough for you to apply your Python skills to research projects. In this course, after first reviewing the basics of Python 3, we learn about tools commonly used in research settings. This version of the course includes a new module on statistical learning.

Using a combination of a guided introduction and more independent in-depth exploration, you will get to practice your new Python skills with various case studies chosen for their scientific breadth and their coverage of different Python features.


Week 1: Python Basics
Review of basic Python 3 language concepts and syntax.

Week 2: Python Research Tools
Introduction to Python modules commonly used in scientific computation, such as NumPy.

Weeks 3 & 4: Case Studies
This collection of six case studies from different disciplines provides opportunities to practice Python research skills.

Week 5: Statistical Learning
Exploration of statistical learning using the scikit-learn library followed by a two-part case study that allows you to further practice your coding skills.

Taught by

Jukka-Pekka JP Onnela


3.7 rating, based on 12 Class Central reviews

4.1 rating at edX based on 47 ratings

Start your review of Using Python for Research

  • Sabine S
    The good things: If I only had to evaluate the video lectures, the course would get 5 stars: excellent lecturer, concepts extremely well explained, very good overview of Python tool boxes for data analysis. Very good melting of white board and cod…
  • Most people don't know about this course. I found it a very great source of Python,Numpy, Pandas and Matplotlib. First two weeks of the course are teaching Python and the necessary libraries for research. Week 3 and Week 4 consist of many case studies which I liked a lot. However, some exercises are really difficult and not relevant to topic.
    Overall, I recommend this course if you have some knowledge of Python and Numpy. It certainly can be challenging for beginners.
  • Anonymous
    Datacamp exercises are especially poor: instructions are often imprecise and ambiguous, with grader having numeric precision errors and unhelpful error reports. There are issues that were reported more than half a year ago that are still not fixed.

    Problems are rather simple, with quite a few Python coding choices that would be frowned upon if you'd do that at work one day.

    It might be an interesting course for a beginner, but there are so many better out there that it's just not worth the time. The course attempts both to teach you some Python and to teach you some basic data science skills. It falls short of both.
  • Andrew Braun
    If you want a quick introduction to some of the most widely-used Python libraries for data, this is a great course to check out. It does have some unfortunate flaws, but sometimes fighting with the auto-grader can actually be productive--it forces y…
  • Ilir Sheraj
    I would like to look at the course from two aspects: 1. Instructor: He is really awesome, explains the concepts clearly and concisely. He is one of the best i have seen in programming, almost on the same level as the legendary Eric Grimson, just no…
  • A great course even with few red points. The course is a good introduction to Python and some concepts but I'd have liked more detailed/advanced case studies - like the one on DNA translations - with more theory.
    One case study isn't available due to a bug as another review stated.
    Videos are really well, the quality of images and sound is better than many other courses and the professor is really good!
    The code you produce is reviewed through Data Camp, which I don't like very much but it may not be your case.
    Despite all these points, I really enjoyed this course which is covering many fields, thank you.
  • Anonymous
    Lectures are well structured and clear. I definitely learned a bunch of new things.

    Comprehension tests are mostly boring and ridiculously simple only checking if a student was sleeping during the video. Nothing to reflect about, no questions to evoke some deeper understanding.

    The homework assignments are poorly worded, which sometimes leads to inconclusive results. Some exercises are missing with the next ones relying on the missing ones. The questions are often ambiguous.
  • This isn't an introductory courses, but gives you a first-week overview of Python. It's useful for intermediate-to-advanced levels, in order to try out case studies related do classification and data analysis of various datasets and areas, such as biology and DNA sequencing or natural language processing. Instructor is great, but the exercises are sometimes dull and not really creative, but I guess it's useful if you want to dive into a specific area. All in all, a nice course.
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    Osama Heba
    This is probably one of the best courses I have audited. It is well presented and it relies showcase the different concepts and techniques in carefully chosen real projects. I would say it suits is for students with a little bit of background or those how have the capacity to catch up with a slightly sharp learning curve.
  • -This adds more knowledge to my introductory knowledge of python.
    -Videos and Prof. is good.
    -But the datacamp exercise are boring and instructions are not very clear.
    -I did 2 weeks then lost interest.
  • Anonymous
    A LOT of content, excellent professor and teaching, homework sometimes annoyingly difficult, sometimes easy, took longer than I had first thought.
  • Anonymous
    The course provides a great deal to learn Python and apply the programming knowledge in various areas. The case studies fulfill the application part. Difficulty level is medium. Course requires good knowledge of Python beforehand and basics are taken in depth. Gaining a lot from this course.

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