Track: Business Intelligence

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, 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 in several ways: you will learn about multi-objective optimization, evolutionary algorithms and supervised learning, i.e., inferring a function from labeled training data. Aside from traditional methods such as nearest neighbour, naive bayes or decision trees, more powerful machine learning algorithms such as random forests, support vector machines or (deep learning) neural networks will be taught (and applied). Similar to Data Analytics 1, practical exercises using R are integrated into the lectures and tutorials and the students will solve case study projects in the second part of the semester. 

    Prerequisites:

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