About This Course
Python has become one of the world's most widely used and versatile programming languages, powering everything from web applications and business automation to data science, artificial intelligence, and cloud computing. Its readable syntax and vast ecosystem of libraries make it the preferred entry point for new programmers and the go-to tool for experienced developers tackling complex analytical or engineering challenges. For organisations across Africa and the Middle East, proficiency in Python represents a critical competitive advantage in the digital economy.
TrainingComplex's Python Programming Training is a comprehensive ten-day programme designed to take participants from the very basics of the language through to practical, job-ready skills in automation, data manipulation, file handling, and introductory data visualisation. The curriculum is structured to mirror real workplace scenarios, ensuring that every concept introduced is immediately anchored in a practical exercise or mini-project.
The course is suitable for professionals with no prior programming experience as well as those who have dabbled in coding and want a structured, rigorous grounding in Python. By working through thirteen progressively challenging modules, participants build genuine fluency rather than superficial familiarity. Each day combines instructor-led instruction with guided coding labs, peer collaboration, and individual assignments that reinforce learning.
Upon completion, participants will have produced a portfolio of working Python scripts and small applications that they can present as evidence of competence to employers or use directly within their organisations. TrainingComplex's expert facilitators bring both industry experience and pedagogical skill to ensure that every participant — regardless of starting point — finishes the programme as a confident, capable Python programmer.
Who Should Attend
- Business analysts and data officers seeking to automate reporting and data workflows
- IT support staff and systems administrators looking to script and automate routine tasks
- Finance and operations professionals who want to manipulate and analyse data programmatically
- Research assistants and monitoring and evaluation officers handling large datasets
- Recent graduates and early-career professionals building their technical skills portfolio
- Project managers and team leads who want to understand and oversee Python-based deliverables
- Educators and trainers developing technology-enhanced learning resources
- Entrepreneurs and startup founders building digital products or internal tools
Learning Objectives
- Understand Python's syntax, data types, variables, and core programming concepts
- Write clean, readable Python scripts following best practices and PEP 8 style guidelines
- Apply control flow structures including conditionals, loops, and exception handling
- Define and use functions, modules, and packages to write modular, reusable code
- Work with Python's built-in data structures — lists, tuples, dictionaries, and sets — confidently
- Read from and write to files, including CSV and JSON formats commonly used in business
- Apply object-oriented programming principles to design and build structured Python programmes
- Use the NumPy and Pandas libraries to import, clean, transform, and analyse tabular data
- Create informative data visualisations using Matplotlib and Seaborn
- Automate repetitive business tasks such as file management, report generation, and web scraping
- Debug Python programmes systematically using built-in tools and best-practice strategies
- Connect Python to external APIs and retrieve live data for processing and analysis
- Deliver a complete, working Python mini-project demonstrating end-to-end programming competence
Course Modules
Module 1: Introduction to Python and the Development Environment
- History, philosophy, and real-world applications of Python
- Installing Python, setting up VS Code or JupyterLab, and understanding the REPL
- Writing and running your first Python script
- Understanding Python's file structure, indentation rules, and comment conventions
- Navigating the Python standard library documentation and using help() and dir() at the REPL
- Managing virtual environments with venv to isolate project dependencies
Module 2: Variables, Data Types, and Operators
- Numeric types: integers, floats, and arithmetic operations
- Strings: creation, concatenation, formatting with f-strings
- Booleans, comparison operators, and logical operators
- Type conversion and checking with type() and isinstance()
- Understanding Python's None type and how it differs from zero, False, and empty strings
- Operator precedence and how parentheses control evaluation order in complex expressions
Module 3: Control Flow: Conditionals and Loops
- if, elif, and else statements with nested conditions
- for loops and iterating over sequences
- while loops, break, continue, and pass
- Practical control flow exercises: grade calculators, number guessing games
- Using the enumerate() and zip() built-ins to iterate with index and pair multiple sequences
- Understanding loop-else clauses and when to apply them for search and validation patterns
Module 4: Functions and Code Modularity
- Defining and calling functions with parameters and return values
- Default arguments, keyword arguments, and *args / **kwargs
- Variable scope: local, global, and the LEGB rule
- Importing and creating modules to organise reusable code
- Writing docstrings and using help() to produce self-documenting, maintainable functions
- Introduction to lambda functions and when to prefer them over full function definitions
Module 5: Python Data Structures
- Lists: creation, indexing, slicing, and common methods
- Tuples, sets, and their use cases in business data
- Dictionaries: key-value pairs, nested dictionaries, and iteration
- List comprehensions and dictionary comprehensions for concise data processing
- Choosing the right data structure: time and space complexity trade-offs for lists, sets, and dictionaries
- Using collections module types — Counter, defaultdict, and namedtuple — for specialised data handling
Module 6: File Handling and Data Formats
- Reading and writing plain text files with open(), read(), and write()
- Working with CSV files using Python's csv module
- Parsing and generating JSON data for APIs and configuration files
- Handling file paths portably with the os and pathlib modules
- Using context managers and the with statement to ensure safe file opening and automatic resource cleanup
- Reading and writing Excel files with openpyxl for integration with spreadsheet-based business workflows
Module 7: Exception Handling and Debugging
- Understanding Python's exception hierarchy and common error types
- try, except, else, and finally blocks for robust error management
- Raising custom exceptions and writing informative error messages
- Using print debugging, the pdb debugger, and IDE breakpoints to trace faults
- Writing and running unit tests with the unittest module to catch regressions early
- Logging application events and errors with Python's logging module instead of ad-hoc print statements
Module 8: Object-Oriented Programming in Python
- Classes, objects, attributes, and methods — core OOP concepts
- Constructors (__init__), instance variables, and the self parameter
- Inheritance, method overriding, and the super() function
- Encapsulation, property decorators, and designing clean class hierarchies
- Special dunder methods — __str__, __repr__, __len__, and __eq__ — for Pythonic class behaviour
- Class methods, static methods, and when to use each in real-world application design
Module 9: NumPy for Numerical Computing
- Creating and manipulating NumPy arrays and understanding ndarray structure
- Array indexing, slicing, and broadcasting operations
- Statistical functions: mean, median, standard deviation, and aggregation
- Practical numerical exercises: processing survey scores and financial series
- Reshaping arrays with reshape(), transpose(), and stacking operations for matrix computations
- Using NumPy's random module to generate synthetic datasets for simulation and testing
Module 10: Data Analysis with Pandas
- Creating Series and DataFrame objects from CSV, Excel, and dictionary sources
- Selecting, filtering, and slicing data with loc, iloc, and boolean indexing
- Cleaning data: handling missing values, duplicates, and data type conversion
- GroupBy operations, pivot tables, and merging DataFrames for analytical reporting
- Applying custom functions to DataFrames with apply() and vectorised string operations via str accessor
- Exporting analysis results to CSV, Excel, and JSON for downstream reporting and sharing
Module 11: Data Visualisation with Matplotlib and Seaborn
- Creating line charts, bar charts, scatter plots, and histograms with Matplotlib
- Customising figures: titles, labels, legends, colours, and subplots
- Building statistical visualisations — heatmaps, box plots, and pair plots — with Seaborn
- Exporting publication-quality figures and embedding charts in reports
- Choosing the right chart type for the data story: comparing distributions, trends, correlations, and compositions
- Annotating figures with text, arrows, and reference lines to highlight key insights for business audiences
Module 12: Automation, APIs, and Web Scraping
- Automating file system tasks: batch renaming, organising folders, and generating reports
- Making HTTP requests with the requests library and handling API responses
- Parsing HTML with BeautifulSoup to extract structured data from web pages
- Scheduling Python scripts and handling rate limits and authentication tokens
- Storing and querying scraped or API-retrieved data using SQLite with Python's sqlite3 module
- Handling errors and retries in network requests to build resilient, production-ready automation scripts
Module 13: Capstone Project: End-to-End Python Application
- Project scoping: defining requirements, selecting a dataset, and planning the solution architecture
- Building the application: ingesting, cleaning, analysing, and visualising real data
- Writing clean, commented, and modular code ready for peer review
- Presenting the finished project — demonstrating outcomes, explaining design choices, and receiving facilitator feedback
- Version-controlling the project with Git: committing incrementally, writing meaningful commit messages, and managing a repository
- Reflecting on course learning outcomes and mapping acquired Python skills to professional roles and next development steps
Requirements & Prerequisites
Participants should have basic computer literacy, including comfort using a keyboard, navigating file directories, and working with spreadsheets or text documents. No prior programming experience is required — the course begins from first principles and builds progressively. A willingness to engage with problem-solving exercises and spend time practising outside formal sessions will significantly enhance the learning experience.
Training Methodology
- Structured live coding sessions where the facilitator writes code in real time and participants replicate and adapt it on their own machines
- Guided laboratory exercises following each concept introduction, with increasingly complex coding challenges to consolidate understanding
- Debugging workshops in which participants are given intentionally broken scripts and must identify and fix errors systematically
- Pair programming activities that encourage peer learning and collaborative problem-solving
- Mini-project sprints at the end of each major topic area, producing small but functional Python scripts tied to realistic business scenarios
- Data analysis walkthroughs using real-world datasets drawn from sectors represented in the participant group
- Code review sessions where participants present their solutions and receive structured feedback from the facilitator and peers
- A capstone project in the final two days requiring each participant to design, build, test, and present a complete Python application
Organisational Benefits
- Reduced manual effort through automation of repetitive data and reporting tasks
- Improved data quality and analytical capacity across business and programme functions
- Strengthened in-house technical capability, reducing reliance on external developers
- Faster turnaround on data-driven decisions through efficient Python-based workflows
- A measurable uplift in staff digital literacy aligned with organisational modernisation goals
- Enhanced ability to integrate data from multiple sources including APIs, spreadsheets, and databases
- Development of internal champions who can mentor colleagues and sustain Python adoption
- A competitive edge in recruitment and project delivery for technology-facing roles
Where Is Python Programming Training Offered?
This course runs in-person across: Johannesburg, Mombasa, Lagos, Nakuru, Kigali, Cape Town, Naivasha, Kampala, Dar es Salaam, Accra, Riyadh, Zanzibar, Nairobi, Arusha, Pretoria, Dubai, Abuja, Kisumu, Addis Ababa, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Johannesburg, South Africa |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
USD 7,000 |
| Mombasa, Kenya |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
KES 230,000 |
| Lagos, Nigeria |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
USD 5,000 |
| Nakuru, Kenya |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
KES 230,000 |
| Kigali, Rwanda |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 3,800 |
| Cape Town, South Africa |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 7,600 |
| Naivasha, Kenya |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
KES 240,000 |
| Kampala, Uganda |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 3,800 |
| Dar es Salaam, Tanzania |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 4,600 |
| Accra, Ghana |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 7,600 |
| Riyadh, Saudi Arabia |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 9,000 |
| Live Virtual |
Online |
24 Oct 2026 – 13 Dec 2026 |
KES 160,000 |
| Zanzibar, Tanzania |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 5,000 |
| Nairobi, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 230,000 |
| Arusha, Tanzania |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 4,200 |
+237 more dates — view live schedule →
Frequently Asked Questions
Do I need any programming experience to attend this course?
No prior programming experience is required. The course is designed to begin from absolute first principles, and the facilitator will ensure that all participants — regardless of background — are supported throughout the ten days.
What software and equipment do I need to bring?
Participants should bring a laptop running Windows, macOS, or Linux with at least 8 GB of RAM. All required software — including Python, VS Code, and relevant libraries — is free, and installation guides are shared with participants before the course begins.
Will I receive a certificate upon completing the course?
Yes. Participants who attend the full programme and complete the capstone project will receive a TrainingComplex Certificate of Completion in Python Programming, which can be shared on professional profiles such as LinkedIn.
Is this course suitable for absolute beginners?
Yes — the course is pitched at beginner level and progresses gradually. However, participants who already have some exposure to Python or another programming language will also benefit from the structured depth of the ten-day curriculum.
Can my organisation send multiple staff members at a group rate?
Absolutely. TrainingComplex offers group enrolment discounts for three or more participants from the same organisation. Please contact our training coordinators directly to discuss group pricing and any bespoke in-house delivery options.
