BSc Applied Data Science

Semester Overview

The first semester aims to develop students’ fundamental knowledge in mathematics and statistics, which is essential for advanced data science courses offered in later semesters. Another focus is on the education of programming and foundations of artificial intelligence. In the first semester, students will also become familiar with basics of business administration to enable a connection between data science and practical management problems at this early stage of their studies. Another important area of the first semester is the legal aspect of data science, where students are introduced to legal frameworks of data science and the vulnerability of IT systems.

The second semester has the overall objective to advance the knowledge gained in the first semester and to deepen students’ knowledge in mathematics and statistics. Drawing on the knowledge gained in the first semester, students learn how to manage and design databases and how various algorithms can be used for developing data structures. Societal and ethical aspects of data science are other important focuses of the second semester.

In the third semester, students become familiar with specific tools that enable them to apply their knowledge to practice (.e.g, web programming, statistical tools). Courses on text mining and media analysis, as well as blockchain applications reflect state-of-the-art domains of data science. A course on time-series analysis and forecasting develops students’ knowledge of tools that help to predict various scenarios that guide business decisions. Finally, a course on research design and writing skills not only prepares students for writing a bachelor thesis, but also educates them on the professional communication of research results.

Semester four of the Bachelor of Science in Applied Data Science prepares students for a potential entrepreneurial career by introducing them to entrepreneurship, innovation, and business planning, as well as management skills such as critical thinking and project management. Introducing students to knowledge extraction, modeling and visualization, and engineering of smart information systems advances students’ data processing knowledge.

The fifth semester focuses on the application of the gained knowledge of various data science tools and methods in a variety of industries. Students can choose four enrichment courses, which will deal with, among others, data science in services, business, health care, and geographic information systems. In this semester, students are expected to write their bachelor thesis. Furthermore, the fifth semester prepares students for the professional event capstone project by offering an internship preparatory course.

The sixth semester is dedicated to the professional capstone project. The purpose of the professional capstone project is to apply the theoretical knowledge to an industry-relevant project by using Python, SQL, R, and/or other specialized analysis toolkits to synthesize concepts from data analytics and visualization. The Data Science Capstone project accounts for the requirement of having real-world experience with analyzing real data sets and solving real-world problems using various data-science methods. Students work closely with the collaboration partner and spend at least 520 hours at their workplace. This teaching approach is in line with international standards, e.g., Yale university requires students of the bachelor program computer science and economics to complete a one-term independent-project course.