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.

Python for Data Analysis - 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 other

The lessons that are included as part of this course are:

1. Advanced Language Features

• Understand the use and significance of docstrings and type hints in Python for better code documentation and type-checking
• Utilize introspection to inspect objects, functions, and modules
• Apply decorators to modify the behaviour of functions and methods
• Implement useful techniques such as casting and error handling with try-except blocks
• Create and use lambda functions, list comprehensions, and generator expressions for efficient Python programming
• Define and use classes in Python, including understanding inheritance and operator overloading

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Virtual Environments

• Understand the importance of using virtual environments in Python development
• Differentiate between various tools for managing virtual environments: venv, Conda, Pipenv, and Poetry
• Create and activate a virtual environment using venv
• Use Conda to manage environments and packages
• Utilize Pipenv and Poetry for dependency management and virtual environments

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Numpy

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Pandas

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Matplotlib

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Scikit-Learn

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Plotly

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Checking and Testing Code

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