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Using Python for Research

Harvard University via edX

8 Reviews 712 students interested

Taken this course? Share your experience with other students. Write review

Overview

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.

Syllabus

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

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Reviews for edX's Using Python for Research
3.4 Based on 8 reviews

  • 5 star 13%
  • 4 stars 50%
  • 3 star 13%
  • 2 star 13%
  • 1 star 13%

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  • 1
Anonymous
5.0 a month ago
Anonymous is taking this course right now.
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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Numan N
4.0 2 years ago
by Numan completed this course, spending 5 hours a week on it and found the course difficulty to be medium.
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.
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Anonymous
2.0 a year ago
Anonymous completed this course.
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.
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Sabine S
1.0 4 months ago
Sabine completed this course, spending 16 hours a week on it and found the course difficulty to be medium.
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 coding.

The bad things:

- The quizzes are mostly trivial. They are on the lowest rank of Bloom's taxonomy, basically only checking factual knowledge, but not checking understanding of concepts.

- Videos where at some point outdated not reflecting current state of a Python package. Instead of updat…
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Andrew B
4.0 a week ago
Andrew completed this course, spending 10 hours a week on it and found the course difficulty to be medium.
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 you to try lots of different approaches before you realize what's going on.

The good:

- Great lecturer. He explains and demonstrates everything very clearly.

- Great topic coverage. I feel like I learned a lot of good basic skills in this course.

The bad:

- The Datacamp auto-grader. I s…
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Anonymous
4.0 2 years ago
Anonymous completed this course.
A LOT of content, excellent professor and teaching, homework sometimes annoyingly difficult, sometimes easy, took longer than I had first thought.
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Prashant S
3.0 12 months ago
by Prashant is taking this course right now, spending 35 hours a week on it and found the course difficulty to be medium.
-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.
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Clément P
4.0 a month ago
by Clément completed this course, spending 2 hours a week on it and found the course difficulty to be medium.
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.
Was this review helpful to you? Yes
  • 1

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