Subject Overview

An advanced, practice-oriented study of Python as the principal programming language for AI, machine learning and analytics. The subject progresses from language foundations to data engineering, modelling, production deployment, governance and emerging AI development practices.

Learning Goal

Students will be able to design reliable Python software, analyse and prepare data, implement AI/ML workflows, evaluate computational and business outcomes, deploy governed solutions, and make informed technical and managerial decisions across the AI lifecycle.

Syllabus

1. Python and the AI/ML Development Ecosystem

Learning Objective: Explain Python’s role in AI and digital transformation; configure interpreters, IDEs, notebooks, virtual environments, package managers and Git; use documentation and establish reproducible project structures.

Category: foundation

2. Syntax, Variables and the Python Object Model

Learning Objective: Apply Python syntax, naming conventions, expressions, operators, dynamic typing, scalar types, mutability, identity, references, type conversion and basic input/output.

Category: foundation

3. Control Flow and Algorithmic Problem Solving

Learning Objective: Develop algorithms using conditions, loops, comprehensions and pattern matching; reason about correctness, complexity, numerical precision and efficient problem decomposition.

Category: foundation

4. Core Data Structures and Collections

Learning Objective: Select and manipulate strings, lists, tuples, sets, dictionaries, ranges and specialised collections; understand indexing, slicing, hashing, copying and memory implications.

Category: core

5. Functions and Functional Programming

Learning Objective: Create reusable functions using parameters, return values, scope, recursion, lambdas, higher-order functions, closures, iterators, generators, decorators, type hints and documentation.

Category: core

6. Modules, Packages and Object-Oriented Design

Learning Objective: Build modular applications with imports and packages; model AI/ML components using classes, inheritance, composition, polymorphism, abstract interfaces, dataclasses and established design principles.

Category: core

7. Reliability, Testing and Software Quality

Learning Objective: Handle exceptions and resources with context managers; debug, log, profile and test programs using unit, integration and property-based techniques; apply linting, formatting, static analysis and secure coding practices.

Category: core

8. Data Access, Persistence and Integration

Learning Objective: Process text, CSV, JSON, binary and columnar formats; use regular expressions, SQL databases, web APIs and serialization while addressing validation, privacy, security and data provenance.

Category: application

9. Numerical Computing with NumPy and Scientific Python

Learning Objective: Perform vectorised computation with arrays, broadcasting, indexing, linear algebra, random sampling and numerical methods; compare vectorisation with loops and manage computational efficiency.

Category: core

10. Data Wrangling with pandas

Learning Objective: Acquire, clean, transform, join, aggregate and reshape tabular and time-series data; treat missing values, outliers and categorical variables while constructing reproducible data pipelines.

Category: core

11. Visualisation, Statistics and Exploratory Analytics

Learning Objective: Use Matplotlib, Seaborn and interactive tools to communicate distributions, relationships and uncertainty; conduct descriptive analysis, sampling, hypothesis-oriented exploration and stakeholder-focused reporting.

Category: application

12. Machine Learning Workflows in Python

Learning Objective: Build scikit-learn pipelines for preprocessing, feature engineering, supervised and unsupervised learning; apply cross-validation, hyperparameter tuning, suitable metrics, explainability and leakage prevention.

Category: application

13. Deep Learning and Contemporary AI Frameworks

Learning Objective: Use Python with PyTorch or TensorFlow for tensors, automatic differentiation, neural network training and evaluation; understand GPU execution, pretrained models, transformers, embeddings and responsible use of generative AI APIs.

Category: advanced

14. Performance, Parallelism and Scalable Computing

Learning Objective: Measure and optimise time and memory; apply vectorisation, multiprocessing, asynchronous programming, JIT compilation and distributed or cloud data frameworks, selecting approaches according to workload and cost.

Category: advanced

15. Production AI, MLOps and Responsible Governance

Learning Objective: Package and expose models through APIs and containers; implement versioning, CI/CD, experiment tracking, monitoring and drift management; evaluate security, fairness, privacy, auditability, regulation, business value and emerging trends such as AI-assisted development and agentic systems.

Category: strategic