Time: Monday 6:00PM - 8:00PM
Changes are tracked using the git version control system. Material for this syllabus is drawn from previous semester, e.g. Fall 2019, Spring2019, and Fall 2018.
This document is subject to revision!
- Martin Skarzynski
- Emily Yaklich (TA)
- Suhwan (Paul) Lee (TA)
This course is designed for non-programmers, biologists, or those without specific knowledge of Python to learn how to program. Emphasis is placed on foundational skills for automation, reproducibility, and sustainable development of code for scientific analysis.
There will be in-class demonstrations, and teaching using JupyterLab. The course also consists of out of class learning using the DataCamp learning platform. This allows students to practice and learn at your their pace with programming exercises that provide realtime feedback on mistakes.
By the end of this course you should be able to:
- Look at a task and determine if you can or should automate it
- Use git for keeping track of changes in your project and collaborating with others
- Create working Python programs
- Develop strategies for leveraging pre-existing solutions to analysing your scientific data
- Be aware of tools and strategies that help sustainably develop robust software for scientific analysis
- Have a deep understanding of the basic structures in Python (e.g. lists, dicts, etc)
- Perform data analysis and visualization with Python
- Demonstrate your Python skills with a project
Please review the course setup requirements
Each student is encouraged to bring their own laptop to each class.
Programing without a computer would be an exceptional feat.
Laptops are available to borrow for class on an as needed basis. There are also Data Processing Stations available to use in the library.
| # | Date | Title | Lead |
|---|---|---|---|
| 1 | 2020-02-03 | Introduction | Martin |
| 2 | 2020-02-10 | Jupyter, python, and bash | * |
| 3 | 2020-02-17 | A python whirlwind (Part 1) | Martin |
| 4 | 2020-02-24 | A python whirlwind (Part 2) | Martin |
| 5 | 2020-03-02 | Git | Martin |
| 6 | 2020-03-09 | Using what we have learned | Martin |
| 7 | 2020-03-16 | More advance git usage | Martin |
| 8 | 2020-03-23 | Private methods, and structuring our own code. | * |
| 9 | 2020-03-30 | A python whirlwind (Part 3) | * |
| 10 | 2020-04-06 | A python whirlwind (Part 4) | Martin |
| 11 | 2020-04-13 | Packaging python code | Martin |
| 12 | 2020-04-20 | Final Project Clinic | All Instructors |
| 13 | 2020-04-27 | Final Project Clinic | All Instructors |
| 14 | 2020-05-04 | Final Project Clinic | All Instructors |
Use the grading rubric for the course. Read it. Internalize it. Live by it. It is like being given the answers before the test. There are no tricks here: we grade you according to the rubric!
Please try to ask your questions during class, if at all possible.
Before contacting us, please check to see if your question has already been answered elsewhere, e.g. StackOverflow.
If you cannot find the answer, please make sure to ask your question thoughtfully (https://stackoverflow.com/help/how-to-ask) and provide everything needed to answer e.g. code, error message, dataset, etc.
In general, please use the course gitter channel to communicate with classmates and instructors. In case of personal/private question/concerns, please use the private chat functionality of gitter.
In case of an emergency, please use gitter and an email.
Course materials are available in the course GitHub repository.
There is no required textbook for this course. We do, however, highly recommend Python for Data Science and its companion text A Whirlwind Tour of Python by Jake Vanderplas. Both of these books are available free on GitHub in Jupyter Notebook form. The code for Python for Data Analysis by Wes McKinney is also on GitHub but the text is only available in the printed copy of the book. For maximum enjoyment, consider working through the relevant chapters before coming to class.
We will link to relevant online resources throughout the course.
If you would like additional material on the basics, the following resources may be useful:
- Python for Biologists by Martin Jones.
- Learn Python the Hard Way (ebook freely available from the author) by Zed A. Shaw; a video course is also available.
- Think Python (ebook freely available from the author) by Allen B. Downey.
- Python for Everybody: Exploring Data in Python 3 (ebook freely available from the author) by Charles Severance.
- The Hitchhiker’s Guide to Python by Kenneth Reitz and Tanya Schlusser.
- Automate the Boring Stuff with Python by Al Sweigart.
- Introduction to Machine Learning with Python by Andreas Mueller and Sarah Guido.
For more information about Python, please see the official Python Software Foundation website.
We recommend that you avail of the following tools as they will help you in your path towards Pythonic stardom:
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The PyCharm Integrated Development Environment (IDE)
The very nice folks at JetBrains have given us free licenses for the Professional Edition of PyCharm Integrated Development Environment (IDE), the best (in my humble opinion) Python Integrated Development Environment (IDE).
If you have a .edu email address, please install PyCharm Integrated Development Environment (IDE) Professional using this link.
If not, a installation link will be distributed to you by email and made available on Slack.
Before the first class, please watch the Getting Started with PyCharm video series.
Before the second class, please watch the PyCharm In-Depth VCS video series.
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Since Fall 2017, the very nice folks at DataCamp have been generously supporting our class via their DataCamp for the Classroom initiative.
This program give us free 6 month access to DataCamp's awesome Data Visualization📊, Machine Learning🤖, and Data Science learning materials.
We will discuss the most interesting examples from DataCamp during class and point out others to be reviewed outside of class.
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All of the course materials are available on GitHub. Before accessing the course materials repo, you should know that
- it is likely to be under constant development throughout the semester and
- you are not expected to work through everything contained therein!
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GitHub is offering some free awesome resources to students, that might be of interest to you, depending on your background: