Data Science and AI Programme 

Module 1: Introduction to Data Science

Why learn Data Science

What is Data Science

Essential Data Science Tools

The Data Science Lifecycle

Adopting a Data Scientist's Mindset

Core Principles: Collaboration, Reproducibility and Ethics

 

Module 2: Working with Data Types and Operators in Python

Introduction to Python

Running Jupyter Notebooks

How to Use a Jupyter Notebook

Basic Data Types

Comparison and Logical Operators

Lists and Indexing

Advanced Indexing

Updating Data in a List

Introduction to Tuples

Introduction to Dictionaries in Python

 

Module 3: Writing Functions in Python

Functions and Arguments

Methods

Writing User-Defined Functions

Conditionals: If Statements

Conditionals: While Loops

For Loops

Looping through a Dictionary

 

Module 4: Popular Data Science Packages in Python

Packages

NumPy Arrays

2D NumPy Arrays

Looping over NumPy Arrays

Pandas Creating Data Frames

Pandas Slicing and Filtering Data Frames

NumPy and Pandas Statistical Tools

 

Module 5: Advanced Functions

Functions

Global Scope vs Local Scope

Nested Functions

Default and Flexible Arguments

Handling Errors and Exceptions

Writing Lambda Functions

 

Module 6: Data Manipulation and Analysis with Pandas

Importing and Exporting Data

Series

Data Frames

Common Functionality

Indexing and Selecting Data

Editing Data Frames: Setting Columns

Editing Data Frames: Transforming Columns

Editing Data Frames: Setting Data with loc

Combining Data Frames

Reshaping Data Frames

Grouping and Aggregating Data

 

Module 7: Data Visualisation with Matplotlib

Introduction to Matplotlib

Simple Line Plots

Bar Plots

Scatter Plots

Histograms

Customising Graphs

Line of Best Fit

Box Plots

Pair Plots

Time Series Plots

Introduction to 3D Plotting

Exporting Figures