STAT40800

Learning Outcomes:

By the end of the module, students should be:
- Competent Python programmers
- Familiar with a range of Python packages and functions for data analysis and visualisation
- Able to obtain, manipulate and analyse large data sets using Python
- Proficient in a range of different data analysis techniques, such as regression, classification and machine learning
- Capable of visualising and interpreting the results of a statistical analysis

Indicative Module Content:

- structure of the Python language
- data manipulation
- data visualisation
- statistical analysis
- regression and classification
- machine learning and clustering algorithms
- APIs and webscraping
- string manipulation and regular expressions

Student Effort Hours:

Approaches to Teaching and Learning:

Requirements, Exclusions and Recommendations

Students should have completed an introductory level statistics course and have a general understanding of calculus.

Module Requisites and Incompatibles

Assessment Strategy

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback
• Self-assessment activities

How will my Feedback be Delivered?

There are non-assessed (practice) and assessed (for credit) coding exercises every week. The exercises are setup on CodeRunner, which allows students correct incorrect attempts (small penalty for assessed exercises). Solutions are released automatically after the deadline passes. Unlike the weekly coding exercises, the midterm assignment and project test the students' ability to interpret their results as well as code proficiently. Students will receive individual feedback on their midterm assignment prior to submitting the final project.