Course Purpose

The purpose of the course is to equip learners for developing, manipulating, recovering and maintaining databases.

Learning Outcomes

At the end of the course the learner should be able to:

Explain the concepts of data mining
Apply mining on different kinds of data
Apply mining on different kinds of knowledge
Discuss principles of pattern analysis and machine learning
Demonstrate representation discovery using Harmonic Analysis

This class will expose you to R and Python the leading programming languages in this century.

This module provides an introduction to the basic ideas and methods of mathematical data mining. In this course, we will consider the following problems: classification, cluster and outlier analysis, mining time-series and sequence data, text mining and web mining, pattern analysis.

A lecture-based module open to all students with a suitable grounding. It covers the fundamental data mining ideas (clustering, support vector machine analysis, semi-supervised learning, information retrieval, collaborative filtering, harmonic analysis) and the most important algorithms (the k-means algorithm, support vector machines, PageRank algorithm, k-nearest neighbor classification, Naive Bayes).

Course Materials

All of the readings in this course are free. R, RStudio and Python are free and available online.

R and RStudio

You can find instruction on installing R and RStudio here

Python and Jupyter Notebook

To learn more on installing Python and Jupyter Notebook read here

Brief Video on Data Mining

Watch this video for brief insight on data mining.

Assignment and Course Assessment

You can find the set of class assignments on the assignment section

The course grades are as follows

Grade	Range
A	70-100
B	60-69
c	50-59
F	0-49

The pass mark is 50 and Above

For continuous assessment test they account 40% of the total score and they will be distributed as follows:

Continuous Assessment	Contribution
Mini-project	15%
Presentation	10%
CAT	10%
Assignments	5%