Data Structures and Algorithms with Python for Beginners in Uganda

Course outline and learning expectations

This is a tutor-led course. The outline shows what your tutor will cover; teaching materials and examinations are provided directly to enrolled students.

What you will learn

• After completing this beginner Data Structures and Algorithms with Python course, learners will be able to:
• Explain algorithms, data structures and abstract data types using clear examples.
• Break programming problems into logical, step-by-step solutions.
• Use Big-O notation to describe basic differences in algorithm efficiency.
• Store and process data using Python lists, tuples, dictionaries and sets.
• Implement arrays, stacks, queues and linked lists.
• Apply linear search and binary search to suitable datasets.
• Implement and compare common sorting algorithms.
• Use hash tables for efficient storage, lookup and retrieval.
• Select an appropriate beginner-level data structure for a practical software problem.
• Test, debug and improve Python algorithms.
• Solve entry-level coding interview and programming challenge questions.
• Build a practical Python project based on a Ugandan or African digital-service scenario.

Modules

1. 1

   Introduction to Algorithms and Computational Thinking

   Meaning of an algorithm.Characteristics of a good algorithm.Inputs, processing and outputs.Writing algorithms with plain language, pseudocode and flowcharts.Breaking large problems into smaller steps.Correctness, readability and efficiency.Python programming fundamentals refresher.Practical task: Design an algorithm for processing learner results.
2. 2

   Understanding Algorithm Efficiency and Big-O

   Why software performance matters.Running time and memory usage.Introduction to time and space complexity.Constant, linear, logarithmic and quadratic complexity.Best-case, average-case and worst-case behaviour.Counting operations in simple Python programs.Comparing alternative solutions.Practical task: Compare two methods for searching customer records.
3. 3

   Arrays, Python Lists and Strings

   How sequential data is stored.Creating and accessing Python lists.Traversing, inserting, updating and deleting elements.One-dimensional and two-dimensional data.Working with strings as sequences.Common array and string problem-solving patterns.Frequency counting and duplicate detection.Practical task: Build a simple school-book or shop-inventory tracker.
4. 4

   Stacks, Queues and Deques

   Last-In, First-Out stack operations.First-In, First-Out queue operations.Push, pop, enqueue, dequeue and peek.Implementing stacks using Python lists.Implementing efficient queues using collections.deque.Expression checking and undo operations.Queue applications in customer service and healthcare.Practical task: Create a clinic or service-centre waiting-line simulator.
5. 5

   Linked Lists

   Limitations of fixed sequential structures.Nodes, values and links.Singly linked lists.Traversing a linked list.Inserting and deleting nodes.Searching and updating linked-list data.Comparing linked lists with Python lists.Introduction to doubly linked lists.Practical task: Build a simple music, lesson or announcement playlist.
6. 6

   Searching Algorithms

   Linear search.Binary search.Requirements for binary search.Iterative and recursive search approaches.Comparing search performance.Searching strings, numbers and object records.Handling missing and duplicate values.Practical task: Create a searchable directory of products, districts or learners.
7. 7

   Sorting Algorithms

   Why data sorting is important.Bubble sort.Selection sort.Insertion sort.Introduction to merge sort.Ascending, descending and multi-field sorting.Stability and in-place sorting.Comparing algorithm efficiency.Practical task: Rank products, examination results or transaction records.
8. 8

   Hash Tables, Dictionaries and Practical Problem Solving

   Key-value data organisation.Python dictionaries as hash-table implementations.Hash functions and collisions.Sets and membership testing.Frequency maps.Fast lookup and duplicate removal.Choosing between lists, sets and dictionaries.Testing and debugging algorithm implementations.Build a Python data-processing application for inventory, learner records or synthetic mobile-money-style transactions.