Current Courses: Overview and Objectives

This wiki page provides an overview of the courses currently being offered. Each course has course objectives that outline its goals in broad statements. There are also learning objectives, which are specific, measurable statements that detail the precise knowledge, skills, attributes, and behaviour that students should be able to demonstrate at the end of a particular lesson.

Course Objectives

• Grasp the fundamentals of Python programming, including data types, control structures, and functions.
• Learn how to load, clean, and manipulate data using Pandas for effective data analysis.
• Learn to use NumPy for numerical operations and handling large datasets efficiently.
• Understand the use of Pandas for handling research problem datasets.
• Create a variety of static and interactive visualisations to represent data insights, covering Matplotlib and Plotly.
• Apply machine learning techniques using Scikit-Learn for predictive modelling.
• Implement testing framework, manage dependencies with virtual environment.
• Learn methods to ensure that research and analyses can be reproduced and validated by others.

Learning Objectives

- Understand the fundamental concepts and benefits of using NumPy for scientific computing.
- Create and initialize NumPy arrays from various data sources, including lists and files.
- Perform file operations such as saving and loading NumPy arrays to and from different file formats.
- Manipulate and analyze data using array attributes and methods.
- Execute arithmetic operations, including matrix and statistical operations, on NumPy arrays.
- Apply advanced techniques such as array slicing, indexing, and broadcasting to efficiently handle large datasets.