0/4/0/0

Today's Information and Communication Technology makes it possible for organizations to monitor their activities at almost any level, resulting in large amounts of data being collected. The conversion of raw data into useful information and knowledge is especially facilitated by means of analysis of the various data using different kinds of statistical and 'Intelligent' methods.

This course is about the data analytics process and methods, covering topics from datawarehouses and online analytical processing, to data mining methods such as decision trees, clustering, Bayesian learning, and neural networks, among others.

The students are able to:
• describe the data mining process and its objectives;
• identify and summarize the main biases inherent to data mining algorithms, and give examples of how they appear in specific algorithms;
• describe and employ basic data mining algorithms;
• list different performance metrics to compare data mining algorithms and illustrate how they may vary with particular data mining algorithms, or the properties of the data;
• use a data mining software tool to analyse structured data and produce and compare different concept descriptions related to classification problems.

In class learning activities involve:
- Lectures explaining the concepts and (sometimes mathematical) theories behind the data analytics process and methods;
- Computer laboratories with guided exercises to explore data analytics software.

Out of class learning activities involve:
- A data mining project assignment in small groups of students;
- Peer-review of other groups projects;
- Solving problem sets in preparation for classes and exam.

The final grade is established based on a weighted average of the exam grade (2/3), and the practical assignment (1/3).