Python Applied to Research
Written by Sue Gee   
Wednesday, 30 November 2016

A four-week MOOC from Harvard University has just started with the aim of enabling students to apply Python skills to research projects. 

Using Python 3 it aims to bridge the gap between introductory and advanced courses using case studies chosen for their scientific breadth and their coverage of different Python features.


PH526x: Using Python For Research is self paced and you have seven months in which to complete it. As it is on the edX platform you can study for free with the option of purchasing a Verified Certificate ($49) at any time. This requires a webcam and government-issued photo ID, such as a passport or driving license.

The instructor for the course is Jukka-Pekka "JP" Onnela, Assistant Professor of Biostatistics and here he introduces the course. Describing Python as powerful, flexible and fun he points out that after this course you'll be in a position to take more advanced courses in Data Science, Machine Learning, Computational Physics and Biomedical Sciences.

Students are expected to have some knowledge of Python and its first week is expected to be a refresher that goes over Python 3 programming basics. The second week looks at Python tools for research applications, in particular  and NumPy and SciPy and the final two weeks are practical case studies in a variety of disciplines. 

The course has an estimated workload of 4-8 hours per week over 4 weeks. It consists of video lectures, comprehension checks after each one and interactive homework created with DataCamp, an online data science school. All comprehension checks and interactive homeworks are graded. The comprehension checks account for 40% of the total grade, the interactive homeworks or 60% and students need an overall score of 70% to pass the course. 

The syllabus for the course is:

Week 1: Basics of Python 3 

  • Part 1: Objects and Methods 
  • Part 2: Sequence Objects 
  • Part 3: Manipulating Objects  

Week 2: Python Libraries and Concepts Used in Research 

  • Part 1: Scope Rules and Classes 
  • Part 2: NumPy 
  • Part 3: Matplotlib and Pyplot 
  • Part 4: Randomness and Time 
  • Week 2 Homework 

Week 3: Case Studies Part 1

  • DNA Translation Homework
  • Language Processing
  • Introduction to Classification 

Week 4: Case Studies Part 2 

  • Classifying Whiskies
  • Bird Migration 
  • Social Network Analysis 

With something to interest students coming from many different field, this course seems very attractive and a good addition to the large number of Python courses available online. 

More Information

PH526x: Using Python For Research

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Last Updated ( Wednesday, 30 November 2016 )