This course covers both theoretical and practical aspects of Business Intelligence, in particular Data Warehousing and Data Mining technologies and techniques. It is divided into two parts:

1. Data Warehousing and Data Mining fundamentals, and
2. Applied lab exercises.

The Data Warehouse and Data Mining Fundamentals part introduces core Data Warehouse and Data Mining technologies, concepts, architectures, and techniques. Students will gain an understanding of

• the importance of analytics in extracting insights from vast amounts of data in today's businesses, 
• how data warehousing and data mining can support decisions and create new business opportunity,
• how to approach business problems and questions with data,
• the fundamentals of data warehouse architectures,
• architectural options, components, and processes in building business intelligence systems,
• the distinctions between OLTP databases and OLAP systems,
• fundamental data mining technologies and their application in business,
• how to select and apply appropriate data mining algorithms and techniques

The Practical Exercise aims to develop working knowledge and skills for designing and implementing Data Warehouse and Data Mining solutions to address business needs.

• Business Intelligence reference architecture
• OLAP (multidimensionality)
• FASMI (Fast Analysis of Shared Multidimensional Information)
• Semantic Modeling of OLAP Solutions
• Logical Modeling (STAR, SNOWFLAKE)
• ETL Process
• Closed-Loop Decision Making
• Big Data technologies
• Data Mining - Knowledge Discovery in Databases
• Patterns and taxonomies
• Predictive and descriptive rules (classification, regression, association, clustering)
• Business Intelligence applications

In the data warehousing part, students will learn to:

• Define a data warehouse in terms of the characteristics that differentiate it from other information systems
• Describe the benefits of data warehousing
• Describe the structure of a data warehouse
• List the features of different types of warehouse data
• Define types of data models in data warehouses
• Define the dimensional model and its components
• Formulate OLAP queries
• Identify types of schema (Star, Snowflake)
• consider organisational aspects

In the Data Mining part, students will learn to:

• Understand what is data mining
• Why using data mining
• Applications of data mining
• Data mining techniques

All teaching materials will be available on TUWEL (available: October 9, 2013).

The first session (attendance strongly recommended) will cover organization and modalities and take place on [tba]

ECTS-Breakdown

18h   Lecture
50h   Exercises: Data Warehousing and Big Data (Assignment or custom project)
40h   Exercises: Data Mining
1h     Exercise interviews
39h   Required reading and preparation for exams
2h     Exams

150 Stunden (= 6 ECTS)

Students should have a solid grasp on:

1. Conceptual database design
2. Relational database model
3. Normalization
4. DBMSs
5. SQL

There will be an opportunity to recap that knowledge at the beginning of the exercises.