Nowadays, a huge amount of data is available online. This data is often of great value for the decisions that business executives or financiers are required to make while the nature, structure and volume of data makes their analysis by traditional methods ineffective and impossible. As a result, today's business executives are called upon to make decisions in a rapidly evolving environment where data is growing exponentially. For this reason they are invited to collaborate with senior IT executives (CTOs), analytics executives and decision makers etc. It is clear from the above that today's business executives need to have strong knowledge and skills in analytics using the internet.

Purpose

The purpose of the course is to give students an initial but comprehensive framework of knowledge and skills regarding data mining and its applications in business.

Objectives of the course

The main objectives of the course are:

•  to understand the core ideas and methods of data mining; and
•  to acquire data mining skills through real-world examples and applications from the business environment.

Characteristics of the curriculum

The proposed curriculum will cover the following key topics:

•  Data characteristics
•  Data transformations and dimensionality reduction
•  Decision trees
•  Correlation
•  Clustering (hierarchical and non-hierarchical)
•  Forecasts
•  Data anomalies

Course syllabus

1. Introduction to data mining (applications, challenges)
2. Structure of a data mining project (stages, stakeholders, roles)
3. Data types and collection methods
4. Representation, data transformations and dimensionality reduction
5. Predictive and categorization methods (Multiple regression, decision trees, logistic regression, discriminant analysis)
6. Data relationships (association rules, clustering)
7. Forecasting (time series, regression, smoothing methods)
8. Data anomalies

On completion of the course the students will be able to:

•  understand the importance of timely and targeted data analysis to improve business decisions;
•  recognize the stages of a data mining project;
•  analyse business problems in detail and perform their own research using data mining methods; and
•  understand and select the most suitable data mining tools to solve a problem.