CDS 411 course materials

• Topic schedule
• Readings
• Homeworks
• Final project
• Resources and links
  • Datacamp cheat sheets
  • Software
• License

Topic schedule

Class	Topic
1	Course toolbox
2	Python fundamentals I
3	Python fundamentals II
4	Python for scientific computing I
5	Python for scientific computing II Demo file: scientific_computing_with_numpy.py
6	Python for scientific computing III
7	System dynamics models: Growth and decay
8	System dynamics models: Growth and decay II Source file: bacteria.py
9	System dynamics models: Drug dosage I Source file: aspirin.py
10	System dynamics models: Drug dosage II Source file: dilantin.py
11	System dynamics models: Damped oscillator and bungee jumping I
12	System dynamics models: Damped oscillator and bungee jumping II Demo notebook: Interactive undamped oscillator notebook Source file: undamped_oscillator.py
13	System dynamics models: Damped oscillator and bungee jumping III and shark competition model Source file: oscillator.py Source file: bungee.py Source file: sharks.py
14	Data-driven modeling I
15	Data-driven modeling II
16	Data-driven modeling III Source file: bootstrap.py
17	Data-driven modeling IV
18	Monte Carlo simulations I Practice notebook: Module 9.1: Quick Review Questions
19	Monte Carlo simulations II Source file: mc_integration.py
20	Monte Carlo simulations III
21	Cellular automata I: Heat diffusion
22	Cellular automata II: Forest fire Source file: forest_fire.py
23	Cellular automata III: Ants
24	Course wrap-up

Readings

Week	Book	Assignment
1		Read all of chapters 1.1 and 1.2
2		Supplement Chapters 1 through 8 cover the material in the Python fundamentals classes in more depth and with a focus on helping beginners.
2		Supplement Chapters 2, 3, 5, 7, 8, 10, 11, 12, and 14 cover the material in the Python fundamentals classes. This book is a reference manual for Python, and covers things at a more advanced level.
3	An introduction to Numpy and Scipy by M. Scott Shell	Read from the beginning up until the end of the Statistics section on page 19
4		Read all of chapters 2.2 and 2.3
5		Read all of chapter 2.5
6		Read all of chapter 3.2
9		Read all of chapters 8.2 and 8.3
10		Read all of chapter 9.2
11		Read all of chapters 9.3 and 9.5
12		Read all of chapter 10.2
13		Read all of chapter 10.3

Homeworks

#	Description
1	Python fundamentals and Python for scientific computing exercises
2	System dynamics: growth and decay models
3	System dynamics: oscillatory motion models
4	Data-driven modeling
5	Monte Carlo simulations: integration and random number generation
6	Monte Carlo simulations: random walk
7	Cellular automata simulations

Final project

Instructions: project/final_project.md

Resources and links

Datacamp cheat sheets

Datacamp has put together a series of Python for Data Science cheat sheets that you can use as a quick reference during the class. The most relevant ones have been downloaded to this repository and are linked below:

Cheat sheet	Description
	Basics of the Jupyter Notebook
	NumPy Basics
	SciPy - Linear Algebra
	Data visualization with Matplotlib
	Data visualization with Seaborn
	Importing Data
	Data transformation and reshaping with Pandas
	Machine learning with Scikit-Learn

Software

The following software is not required for participating in the course, but may be useful in your workflow.

Software	OS	Description
GitKraken	Windows macOS Linux	A graphical interface for using git. Cross-platform, works with GitHub, and free to use for educational purposes. Cheatsheets available: GitKraken cheat sheet GitKraken for GitHub users cheat sheet
GitHub Desktop	Windows macOS	A graphical interface for interacting with GitHub, built by GitHub. User documentation from GitHub is available: Getting Starting with GitHub Desktop Contributing to projects with GitHub Desktop
Visual Studio Code	Windows macOS Linux	A cross-platform and open-source integrated development environment (IDE) for programming. Uses a plugin system called Extensions to add support for different languages and for interfacing with git and GitHub. At a minimum, you should install the official extension for Python. There are also introductory tutorial videos available.
PyCharm	Windows macOS Linux	A cross-platform integrated development environment (IDE) designed specifically for programming in Python. Comes with many useful features enabled. Has a plugin ecosystem, but unlike Visual Studio Code they can be treated as optional. There are introductory tutorial videos available. As a current student, you get a free professional license for the editor if you fill out and submit this form.

License

Unless otherwise noted, the course materials in this repository are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.