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:presence-teaching if possible according to regulations in place in winter term
• flipped Classroom
• Assignments to be elaborated in small groups

• All required information will be presented in the preliminary lecture (Vorbesprechung)
• All teaching materials will be available on TUWEL.

Preliminary Lecture (Vorbesprechung) 

• Thu., 12.10., 12:00, Lecture Hall GM5
• The first session will address all questions concerning admission / registration to the course, course modalities, lecture dates and other administrative issues.

ECTS-Breakdown

18h   Lecture
90h   Exercises
38h   Reading and self-study
4h     Presence for oral and written assignments

150 Hours (= 6 ECTS)

• written assignments and oral reviews of these assigments.
• written assigment/exam (closed book) - according to current planning in presence. If the pandemic situation will not allow exams in presence at the scheduled time, we will switch to an on-line assessment via TUWEL. In case of low enrollment, this assigment can also be conducted orally (also depending on the development of the pademic situation.

Acceptance to the course will be by the lecturers. Priority is given to

1. Students that have this course as a compulsory course (e.g. foundations, core modules) in their curriculum.
2. Students that have this course as an elective course.
3. Students that are currently in a bachelor programme of any of the studies mentioned in 1.), and are finishing their studies in the current semester.
4. If there are still free places afterwards, they will be assigned according to the ranking on the waiting list

NOT recommended for ERASMUS students due to space restrictions!

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.