Business Intelligence (BI) and Data Analytics (DA) refer to a variety of methods and techniques for the analysis of large amounts of business data such as data warehousing (DWH), reporting, (pre-)processing and extracting information from data, Online Analytical Processing (OLAP), as well as modeling underlying patterns by means of powerful machine learning models. In this track, you will learn conceptual and mathematical foundations of BI and DA. Also, you will get to know relevant techniques to build BI solutions, supporting  the decision making of modern companies. You will implement hands-on cases related to data warehousing based on modern BI tools as well as OLAP, Big Data processing, data mining techniques, and machine learning algorithms for different levels of analytic insight and accurate predication making.

The lectures

• Management Information Systems and Data Warehousing (Winter Term)

  This course introduces fundamental concepts, methods, and techniques for the analysis of business data like Data Warehousing (DWH), reporting, and Online Analytical Processing (OLAP). Topics range from information needs analysis via conceptual and logical design to implementation and optimization of data warehouse schemata, including modern architectures (column stores, in-memory, many/multi-core, streaming data, NoSQL, and Big Data processing). Hands-on experience with modern BI tools and guest lectures by BI professionals are part of the curriculum.

  Prerequisites

  • Database design and querying as taught in course Data Management in the Bachelor’s program (Entity-Relationship modeling, relational database design and normalization, SQL)

  Preparatory Resources

  • Standard textbooks covering database design, e.g., Elmasri/Navathe: Fundamentals of Database Systems, Pearson, 2017

   

• Data Analytics I (Winter Term)

  This course focuses on multivariate statistical methods in the context of data mining. Within the lecture, you will learn how to preprocess, exploratively analyze and understand data, e.g., by means of checking for correlations or identifying outliers. However, the main topic of this course is unsupervised learning, i.e., understanding the inherent structure of unlabeled data. In addition to learning the underlying (theoretical) concepts of the presented methods, you will learn how to actually apply the taught approaches (using the statistical software R) to given data sets.

  Prerequisites:

  • Basic to advanced programming skills in R.
  • Solid background in statistics and mathematics.

• Data Analytics II (Summer Term)

  This course complements Data Analytics 1: You will be learning about supervised machine learning techniques including nearest neighbors models, naive bayes, decision trees, random forests, support vector machines, and (deep) neural networks. Similar to Data Analytics 1, practical exercises will be integrated into the lectures/tutorials and the students will solve case study projects in the second part of the semester. Note that the course content is not based on Data Analytics 1, i.e., it is possible to follow Data Analytics 2 without having taken Data Analytics 1 before

  Prerequisites:

  • Basic to advanced programming skills in Python and R.
  • Solid background in statistics and mathematics.