Module WS 2019/2020

In this course, students apply acquired data science knowledge and skills to solve a real-world business problem from the area of marketing, finance, or operations.Topics may include

• Supervised learning (regression, classification)
• Unsupervised learningText mining
• Social network analysis
• Assessing model quality
• Assessing technologies

Business Process Management provides an introduction to fundamental concepts, frameworks, models, theories, and methods in process management and covers the operation, improvement, and innovation of business processes. Business Process Management (BPM) is one of the core topics of the degree programme, so the course also provides a basis on which students can choose their electives. The course covers eight primary topics:

• Business process operations
• Business process change
• Strategic alignment
• Business process governance
• Quality management
• Six Sigma
• BPM skills
• Organizational culture

Business Statistics covers statistical methods that are used to support decision-making in business contexts, so it also provides a methodological foundation for the students' master's thesis projects. The course builds on the basic concepts of statistical testing and estimation theory that are usually taught in bachelor’s programmes. The course covers five primary topics:

• Graphic and numeric characterizations of random variables and their distributions
• Framework and basic applications for testing hypotheses and estimating parameters
• The ordinary least squares (OLS) method
• Simple linear regression, including parameter estimation, diagnostic plots, hypothesis testing, predictions, and model specifications using log-transformations
• Introduction to the software package R

Data and Application Security provides an introduction to cyber security and covers issues related to computer and information security. Security is one of the core topics of the degree programme, so the course also provides a basis on which students can choose their electives. The course covers nine primary topics:

• Security goals and design principles
• Economic aspects of security and risk analysis
• Basics of cryptography
• Authentication and access control
• Key instruments of network security
• Key instruments of web security
• Software security, vulnerabilities, and attacks
• Email and mobile device security

Students are required to pass this course in order to register for Network and System Security and Intrusion Detection and Mitigation courses.

Data Management covers the modern data-management cycle, from the collection of data from diverse sources to the preparation of data for data-driven applications. Students learn how to handle various data formats, how to assess and improve data quality, and how to store and process data using SQL, NoSQL, and Hadoop technologies. The course covers eight primary topics:

• Modern data-management requirements
• Database system architecture
• Diagnosing and handling data quality problems
• Relational databases (SQL)
• Hands-on labs with MySQL
• Concurrency control techniques
• NoSQL databases (e.g., MongoDB)
• Apache Hadoop (HDFS, MapReduce)

In the Innovation Lab seminar, students work in small groups to solve practical IT problems in cooperation with multiple regional companies. Representatives of these companies regularly provide students with feedback at the university and take part in networking events. The seminar thus also supports dialogue between regional industry and the university, helping students to interact with world-renowned companies right from the start of their studies. Students learn to work independently, to work in a team, to take responsibility, and to present project results effectively. In addition to creative thinking, the use of skills related to problem-solving, organizing and planning, communication, and project management is encouraged. Course topics change from semester to semester.

Management Information Systems focuses on large-scale application software packages that support end-to-end processes, information and document flow, reporting, and data analytics in organizational settings. The course covers eight primary topics:

• Enterprise applications
• E-business
• Managing knowledge
• Enhancing decision-making
• Building management information systems (MIS)
• Managing projects and global systems
• MIS-related integration, transformation, innovation, and change
• Case studies on current MIS topics

In this course, students apply acquired data science knowledge and skills to solve a real-world business problem from the area of marketing, finance, or operations.Topics may include

• Supervised learning (regression, classification)
• Unsupervised learningText mining
• Social network analysis
• Assessing model quality
• Assessing technologies

In Research Methods, students learn to identify pertinent research questions, conduct systematic literature reviews, apply appropriate research methods, and report on their results. The course covers nine primary topics:

• Introduction to scientific research
• Scientific writing
• Ethical standards
• Literature reviews
• Qualitative research
• Quantitative research
• Mixed-methods research
• Design science research
• Theories used in Information Systems research

Short description
The course focuses on developing research proposals in the field of data science.

Topics

• Conducting literature reviews
• Developing research questions
• Designing qualitative, quantitative, and design oriented research
• Writing research proposals
• Ethical issues in data science