Intermediate Data Structures and Algorithms with Python 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 intermediate Data Structures and Algorithms with Python course, learners will be able to:
• Analyse the time and space complexity of multi-step algorithms.
• Design and implement recursive algorithms.
• Build and traverse binary trees and binary search trees.
• Use heaps and priority queues to process high-priority tasks efficiently.
• Represent real-world networks using graphs.
• Implement breadth-first search and depth-first search.
• Find efficient paths through weighted networks.
• Apply divide-and-conquer, greedy and backtracking strategies.
• Solve suitable optimisation problems using dynamic programming.
• Compare algorithmic solutions and justify design decisions.
• Write testable, maintainable and efficient Python implementations.
• Solve intermediate coding interview and competitive-programming problems.
• Build an applied algorithm project for an African business or public-service scenario.

Modules

1. 1

   Algorithm Analysis and Recursion

   Reviewing time and space complexity.Analysing nested loops and multi-stage algorithms.Recursive thinking and recursive functions.Base cases and recursive cases.Understanding the call stack.Recursion versus iteration.Introduction to recurrence relations.Memoisation.Practical task: Implement and analyse recursive search and calculation problems.
2. 2

   Trees and Binary Search Trees

   Tree terminology and structure.Root, parent, child, leaf, height and depth.Binary trees.Binary search tree properties.Insertion, search and deletion.Preorder, inorder and postorder traversal.Recursive and iterative traversal.Balanced and unbalanced trees.Practical task: Build a searchable product or learner-record index.
3. 3

   Heaps and Priority Queues

   Complete binary trees.Min-heaps and max-heaps.Heap insertion and removal.Heapify operations.Python’s heapq module.Heap sort.Priority queue design.Selecting the highest-priority task efficiently.Practical task: Create a clinic, delivery or customer-support priority system.
4. 4

   Graphs and Network Modelling

   Vertices, edges and weights.Directed and undirected graphs.Weighted and unweighted graphs.Adjacency lists and adjacency matrices.Graph construction in Python.Breadth-first search.Depth-first search.Connected components and cycle detection.Practical task: Model connections between towns, services or delivery points.
5. 5

   Shortest Paths and Graph Applications

   Pathfinding problems.Dijkstra’s shortest-path algorithm.Limitations of shortest-path methods.Topological sorting.Dependency graphs.Introduction to disjoint sets and union-find.Comparing graph algorithms.Testing graph solutions.Practical task: Find efficient delivery routes or model course prerequisites.
6. 6

   Divide and Conquer, Greedy Algorithms and Backtracking

   Recognising algorithm-design patterns.Divide-and-conquer strategy.Merge sort review and quicksort.Greedy-choice strategy.Interval scheduling and resource selection.Backtracking and decision trees.Constraint-based search.Pruning impossible solutions.Practical task: Solve scheduling, allocation or route-selection challenges.
7. 7

   Dynamic Programming

   Recognising overlapping subproblems.Optimal substructure.Memoisation and tabulation.One-dimensional dynamic programming.Two-dimensional dynamic programming.Knapsack-style problems.Sequence and grid problems.Comparing recursion, greedy methods and dynamic programming.Practical task: Optimise a limited budget, loading plan or resource allocation.
8. 8

   Applied Algorithm Engineering and Capstone

   Selecting the right data structure.Combining trees, heaps, graphs and hash tables.Measuring algorithm performance.Generating test cases.Handling edge cases.Refactoring inefficient code.Explaining technical decisions.Intermediate coding interview strategies.delivery-route optimiser, clinic priority manager, produce-distribution planner or marketplace search engine.