Course Description

This is a self-paced independent learning course focuses on developing Python programs, enabling learners to explore concepts and practice programming skills on their own. Participants will learn key Python constructs such as variables, data types, lists, tuples, control structures, and functions. By the end of the course, learners will be able to write structured programs and apply Python to solve basic computational problems.

Course Topics

• Variables, Data Types, and Input/Output Operations

• Lists

• Tuples

• Selection Structures (if, if–else, elif)

• Repetition Structures (for, while)

• Functions

Learning Outcomes

Upon successful completion of this course, participants will be able to:

• Write Python programs using appropriate syntax and programming constructs.

• Apply control structures and functions to develop structured and logical solutions.

Course Prerequisites

None

Course Assessment

Assessment for this course is based on one (1) online multiple-choice quiz (20 marks) and a written assignment (30 marks).

Course Description

This is a self-paced independent learning course focuses on developing intermediate Python programs, enabling learners to explore concepts and practice programming skills on their own. Participants will learn key Python constructs such as dictionary data structure, numpy, file handling, data visulaization and manipulation.  By the end of the course, learners will be able to write structured programs and apply Python to solve  computational problems.

Course Topics

• Dictionary

• Numerical Python (Numpy)

• File Handling

• Data Visualizations using Matplotlib

• Data Manipulation using Pandas

Learning Outcomes

Upon successful completion of this course, participants will be able to:

• Apply Python tools and libraries such as Dictionary, NumPy, Matplotlib, and Pandas to handle files, process numerical data, visualize information, and manipulate datasets.

• Develop practical data analysis solutions by reading/writing files, performing computations, creating visualizations, and extracting insights from data.

Course Prerequisites

You must have basic knowledge on Python especially on loops (for/while), selection structure (if/if-else/elif), list data structure and functions.

Course Assessment

Assessment for this course is based on one (1) online multiple-choice quiz (20 marks) and a written assignment (30 marks).

Course Description

This is a self-paced independent learning course that focuses on Python-based machine learning, enabling learners to explore concepts and practice programming skills on their own. Participants will learn key machine learning concepts and practical skills, with hands-on implementation using the Scikit-Learn library.

Course Topics

• Introduction to Machine Learning
• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Ridge and Lasso Regression
• Logistic Regression
• K-Nearest Neighbors (K-NN)
• Decision Tress
• Model Evaluation for Classifiers
• K-Means Clustering

Learning Outcomes

Upon successful completion of this course, participants will be able to:

• Describe fundamental machine learning concepts and principles.

• Develop machine learning programs using Python.

Course Prerequisites

Good knowledge on Python

Course Assessment

Assessment for this course is based on one (1) online multiple-choice quiz (20 marks) and a written assignment (30 marks).

Course Description

This is a self-paced independent learning course  focuses on developing machine learning models using visual approach (non-programming) using an open-source tool. 

Course Topics

1. Introduction to AI and Machine Learning
   

2. Introduction to Orange tool

3. Creating classification models
   

4. Creating regression models

Learning Outcomes

Upon successful completion of this course, participants will be able to:

• Describe the meaning of machine learning and its components

• Create machine learning classifier using visual approach

• Create machine learning regressor using visual approach 

Course Prerequisites

Course Assessment

Assessment for this course is based on four (4) online multiple-choice quizzes. Participants must complete and pass these quizzes to demonstrate understanding of the course content.

Course Description

This is a self-paced independent learning course  focuses on developing agentic AI applications using LangGraph, a well-established and widely adopted Python-based framework for building AI agents. Participants will learn how to design, structure, and operationalize AI agents capable of reasoning and  decision-making as well as branching.

Course Topics

1. Introduction to Agentic AI
   Understanding the concept, benefits, and real-world applications of agentic AI.

2. Fundamentals of LangGraph
   Core components, architecture, states, nodes, edges, and workflows.

3. Conditional Logic
   Designing intelligent agent behavior using branching logic and flow control.

4. LLM Integration & Reasoning
   Connecting LangGraph with LLMs (e.g., OpenAI, Gemini) and enabling reasoning capabilities.

Learning Outcomes

Upon successful completion of this course, participants will be able to:

• Design and develop AI agents using the LangGraph framework.

• Implement conditional branching to create dynamic and intelligent agent workflows.

• Integrate Large Language Models (LLMs) with LangGraph for reasoning and decision-making.

Course Prerequisites

• Basic knowledge of Python programming.

• Exposure to LLM tools such as ChatGPT, Gemini, or similar platforms is helpful but not mandatory.

Course Assessment

Assessment for this course is based on three (3) online multiple-choice quizzes. Participants must complete and pass these quizzes to demonstrate understanding of the course content.

Course Description

This is a self-paced independent learning course  focuses on learning analytics for educators using visual approach (non-programming) using an open-source Orange tool. 

Course Topics

1. Introduction to Learning Analytics
   

2. Introduction to Orange tool

3. Descriptive Analytics with Orange

4. Predictive Analytics in Orange: Classification

5. Predictive Analytics in Orange: Regression

Learning Outcomes

Upon successful completion of this course, participants will be able to:

• Differentiate between analytics, data analytics, and learning analytics

• Describe the four types of analytics

• Perform descriptive analytics using the Orange tool

• Perform predictive analytics using the Orange tool

Course Prerequisites

Course Assessment

Assessment for this course is based on four (4) online multiple-choice quizzes. Participants must complete and pass these quizzes to demonstrate understanding of the course content.