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Using Python for Research

Harvard University via edX

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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
3.3 Based on 4 reviews

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  • 1
Numan N
4.0 a year 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 8 months ago
Anonymous partially 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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Anonymous
4.0 11 months 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.
Was this review helpful to you? Yes
Prashant S
3.0 5 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.
Was this review helpful to you? Yes
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