After successful completion of the course, students are able to...

• apply analytic methods to extract business insights from vast amounts of data
• systematically tackle business problems and questions with data
• evaluate Data Warehousing and Big Data Technologies
• compare and contrast the benefits and limitations of various data wareousing and big data architectures.
• define solid processes to answer analytical questions
• identify concrete business goals and data mining goals
• perform solid analyses using both supervised as well as unsupervised machine learning techniques including the necessary preprocessing steps
• critically reflect on results obtained and interpret them

• Business Intelligence reference architecture
• OLAP (multidimensionality)
• Logical Modeling (STAR, SNOWFLAKE)
• ETL Process
• Closed-Loop Decision Making
• Big Data technologies
• Data Lakes
• 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 about

• Definition von Data Mining, Data Mining Prozesse: CRISP-DM, ASUM-DM
• Data Preprocessing
• Unsupervised Techniques for Data Analysis, Clustering
• Supervised Tech´niques for Data Analyses: Classification
• Evaluation of data analysis processes and models

- Lectures

- flipped Classroom

- Assignments to be elaborated in small groups

All teaching materials will be available on TUWEL.

The first session (attendance strongly recommended) will cover organization and modalities

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)

- writen tests on the lecture blocks

- writen assignments and oral reviews of these assignment

Students should have a solid grasp on:

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

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