Program design and data manipulation with Python. Overview of data structures, iteration, flow control, program design, and using libraries for data exploration and analysis.
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By the end of the course, students are expected to:
- Translate fundamental programming concepts such as loops, conditionals, etc into Python code.
- Understand the key data structures in Python.
- Understand how to write functions in Python and assess if they are correct via unit testing.
- Know when and how to abstract code (e.g., into functions, or classes) to make it more modular and robust.
- Produce human-readable code that incorporates best practices of programming, documentation, and coding style.
- Use NumPy perform common data wrangling and computational tasks in Python.
- Use Pandas to create and manipulate data structures like Series and DataFrames.
- Wrangle different types of data in Pandas including numeric data, strings, and datetimes.
Specific learning objectives can be found in the Lecture Learning Objectives document.
The table below shows the general lecture outline; see the Lecture Learning Objectives document for lecture-specific learning objectives.
| Lecture | Topic | Optional Pre-readings | Practice exercises |
|---|---|---|---|
| 1 | Basics | WTP: Section 3 - Section 7 | |
| 2 | Loops & Functions | WTP: Section 8 - Section 13 PEP 257: Docstrings NumPy docstring examples |
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| 3 | Unit Tests & Classes | Python documentation: 9. Classes Think Python: "Classes and objects", "Classes and functions", "Classes and methods" |
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| 4 | Style Guides, Scripts, Imports | PEP 257: Style Guide Getting Started with Python in VS Code up to "Run Hello World" Python documentation: 5. The import system |
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| 5 | Introduction to NumPy | PDSH: Introduction to Numpy Numpy documentation: Quickstart tutorial |
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| 6 | Introduction to Pandas | PDSH: Data Manipulation with Pandas up to "Operating on Data in Pandas" Pandas documentation: 10 minutes to pandas, up to "Selection" |
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| 7 | Basic Data Wrangling with Pandas | PDSH: Data Manipulation with Pandas Pandas documentation: 10 minutes to pandas |
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| 8 | Advanced Data Wrangling with Pandas | PDSH: Data Manipulation with Pandas Pandas documentation: 10 minutes to pandas |
You are responsible for the following deliverables, which will determine your course grade:
| Assessment | Weight | Due Date | Location |
|---|---|---|---|
| Lab Assignment 1 | 15% | Sunday, Sept 13 at 18:00 | Submit to Github & Canvas |
| Lab Assignment 2 | 15% | Saturday, Sept 19 at 18:00 | Submit to Github & Canvas |
| Quiz 1 | 20% | Tuesday, Sept 22 at 14:00 | Online |
| Lab Assignment 3 | 15% | Saturday, Sept 26 at 18:00 | Submit to Github & Canvas |
| Lab Assignment 4 | 15% | Saturday, Oct 3 at 18:00 | Submit to Github & Canvas |
| Quiz 2 | 20% | Tuesday, Oct 6 at 10:00 | Online |
Quizzes will be held in week 3 and week 5, are open book and are typically 30 mins long with a focus on short-answer questions. More information on quizzes will be provided closer to their dates.
- Python documentation
- Think Python: How to Think Like a Computer Scientist
- A Whirlwind Tour of Python (WTP), Jake VanderPlas (O’Reilly). Copyright 2016 O’Reilly Media, Inc., 978-1-491-96465-1.
- Python Data Science Handbook (PDSH), Jake VanderPlas (O’Reilly). Copyright 2016 O’Reilly Media, Inc., 978-1-491-91205-8.
- Python for Data Analysis, Wes McKinney (O'Reilly). Copyright 2013 O’Reilly Media, Inc, you can download chapters from the book for free from the UBC library.
- Kaggle Learn Python Tutorials