Course material for Intro to Python for Columbia Biological Sciences Department
| Date | Lecture | Topic | Instructor | Slides | Notebook | Assignment | Solution |
|---|---|---|---|---|---|---|---|
| 6/25 | 0 | Getting familiar with Google Colab and Python | - | Getting Started | - | Before the course, everyone should get familiar with Google Colab, which will be the primary way in which we write and run code | - |
| 7/2 | 1 | Data Types, Code Execution, Markdown, Conditionals | Rahim | Lesson 1 | Foundations of Python | Assignment 1 | Assignment 1 Solutions |
| 7/9 | 2 | Pandas, NumPy, SciPy | Shanice | - | NumPy and Pandas | Assignment 2 | Assignment 2 Solutions |
| 7/16 | 3 | Plotting | Jonathan | - | Data Visualization | Assignment 3 | Assignment 3 Solutions |
| 7/23 | 4 | For Loops, Advanced Conditionals, Dictionaries | Rahim | Lesson 4 | For Loops, Advanced Conditions, Dictionaries | Assignment 4 | Assignment 4 Solutions |
| 7/30 | 5 | APIs, Machine Learning | Jonathan | - | API Practice | Choose a Public API or another dataset for your final project | - |
| 8/6 | 6 | Analysis and Application | Rahim/Shanice | - | PubMed API, Stock Market API | Continue to work on final project | - |
| 8/13 | 7 | Final project presentations | - | - | - | - | - |
- Rahim Hashim (rh2898@columbia.edu)
- Shanice Bailey (stb2145@columbia.edu)
- Jonathan Reeve (jpr2152@columbia.edu)
All assignments will be posted here after the lesson for the week. Solutions will be provided the following week.
Additional resources that are often open on my computer while programming
- Software Carpentry
- Google Colab
- Style
- MATLAB
The class will incorporate a final project for groups of 3-4 students. The project will be mostly open-ended, but will require using the tools learned over the course to implement. All code will be posted for future years of the class to look back on. Additionally, the final class will be allocated for short presentations by each group.
More information will be provided once the first class has been administered and there is a better sense of how many students will participate.
Potential datasets for project inspiration: