This is a short 4-week preparatory course for CSC8641 - Big Data Analytics.
| Week | Date | Part 1 | Part 2 | Exercises |
|---|---|---|---|---|
| 1 | 21 Nov | Environment setup with Anaconda | Introduction to Python | link |
| 2 | 28 Nov | Jupyter notebooks | Python data structures | link |
| 3 | 05 Dec | Lazy evaluation | Numerical computing with Numpy | link |
| 4 | 12 Dec | Pandas Series and DataFrames | Data wrangling with Pandas | link |
This course's primary objective is to get you up to speed with base Python concepts and programming skills required for CSC8641 - Big Data Analytics (taught in block 4, Semester 2). In addition to key Python concepts, you will be introduced to the python ecosystem for data science, particularly Numpy for numerical computing and Pandas for manipulation and analysis of tabular data.
This course is structured for self-learning, with new material being made available at the start of each week. The coursework material is a compilation of content from different resources, primarily textbooks. You are expected to work through the material at your own pace and complete the proposed practice/self-assessment exercises. Note, however, that these exercises are not marked.
The NCL Library website provides online access to the following resources, used throughout the course:
- A Beginners Guide to Python 3 Programming by John Hunt (pdf, epub)
- Learn Python the hard way by Zed Shaw (read online)
- Python for Data Analysis by Wes Mckinney (read online)
- Python data science handbook by Jake VanderPlas (html)
- ❗ This is coursework is not marked.
- 📧 [email protected]
- ❔ 💬 Use GitHub issues page and discussions on canvas to ask questions and discuss with your peers.