Python ecosystem: installation, IDEs, virtual environments, and pip · Variables, data types, type conversion, and operators · String methods, f-string formatting, and string slicing · Input and output: command-line interaction and basic I/O · PEP 8 style guide: writing clean, readable, professional Python code

Conditional statements: if, elif, else, and ternary expressions · Loops: for, while, break, continue, pass, and else on loops · Functions: parameters, default arguments, *args, and **kwargs · Lambda functions, map(), filter(), and reduce() · Recursion: base cases, call stack, and practical examples

Lists: indexing, slicing, comprehensions, and common methods · Tuples: immutability, packing, unpacking, and named tuples · Dictionaries: creation, methods, comprehensions, and nesting · Sets: mathematical operations, frozensets, and use cases · Choosing the right data structure: time and space complexity basics

Classes and objects: attributes, methods, and __init__ constructor · Inheritance: single, multiple, and method resolution order · Polymorphism, encapsulation, and abstraction · Magic/dunder methods: __str__, __repr__, __len__, __eq__ · Design patterns: singleton, factory, observer — introduction

Reading and writing: text files, CSV, JSON, and XML · Exception handling: try, except, else, finally, and raise · Creating custom exception classes and hierarchies · Python standard library: os, sys, datetime, math, random, re · Creating, importing, and distributing custom Python modules

NumPy: arrays, vectorised operations, and broadcasting · Pandas: DataFrames, groupby, merge, pivot, and apply · Matplotlib and Seaborn: publication-quality visualisations · Web scraping: BeautifulSoup and Requests libraries · Automation: files, emails, APIs, and scheduled tasks

Choose your track: data analysis, automation, or web scraping · Project planning: scope, architecture, and milestone timeline · Build: complete Python project with clean, documented code · GitHub: README, requirements.txt, and full version control · Code review: live session with written feedback from instructor

Write clean, professional Python code following PEP 8 and OOP principles · Build, test, and document Python applications across multiple domains · Analyse data with NumPy and Pandas and create professional visualisations · Automate tasks and work with external APIs, files, and web data · Achieve a credential benchmarked against Python Institute PCEP and PCAP standards

Complete all 7 modules, submit a documented Python project on GitHub with README and requirements.txt, and pass a live code review (minimum 75%).

Python Developer, Backend Developer, Data Analyst, Automation Engineer, Research Programmer, and pathway to CDA Data Analyst and CDS Data Scientist.