Introduction

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.

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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.

This run of the course includes revised assessments and a new module on machine learning.

What you'll learn:

  • Python 3 programming basics (a review)
  • Python tools (e.g., NumPy and SciPy modules) for research applications
  • How to apply Python research tools in practical settings

Meet The Faculty

Jukka-Pekka Onnela

Jukka-Pekka Onnela

Assistant Professor of Biostatistics, Harvard University

JP is an Assistant Professor of Biostatistics in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. His research focuses on statistical network science and digital phenotyping methods. JP did postdoctoral training at Harvard and Oxford, and received his doctorate at the Helsinki University of Technology. In 2013, he was awarded an NIH Director’s New Innovator Award.

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