Data Science Masterclass

Course overview

Goals of the Training The focus of the Data Science Masterclass training is to impart solid knowledge and skills in the field of data science. The content includes advanced mathematics and statistics, which serve as the foundation for complex data analyses. Machine learning and deep learning are covered to learn advanced techniques for pattern recognition and prediction. Innovation and entrepreneurship foster an understanding of the application of data science in entrepreneurial contexts. A seminar on Data Science and Society highlights the societal impacts and ethical questions. Practical use cases and their evaluation, as well as a project for implementing a data science use case, round off the training. Content of the Training Advanced Mathematics Calculus Integral Transformations Vector Algebra Vector Calculus Matrices and Vector Spaces Information Theory Advanced Statistics Introduction to Statistics Important Probability Distributions and Their Applications Bayesian Statistics Descriptive Statistics Data Visualization Parameter Estimation Hypothesis Testing Machine Learning Supervised, Unsupervised, and Reinforcement Learning Approaches Regression and Classification of Learning Problems Estimating Functional Dependencies Using Regression Methods Data Clustering Support Vector Machines, Large Margin Classification Learning in Decision Trees Deep Learning Introduction to Neural Networks and Deep Learning Network Architectures Neural Network Training Alternative Training Methods Other Network Architectures Innovation and Entrepreneurship Innovation Management and Entrepreneurship in a Globalized World Basics of Entrepreneurship Business Idea and Company Formation Funding Sources and Funding Processes Internet, Digital Business, and Artificial Intelligence Strategic Alliances Family Businesses Seminar: Data Science and Society In this module, participants will reflect on the current societal and political impacts of applying data science models. For this purpose, relevant topics will be presented through articles, which participants will critically evaluate in the form of a written essay. Use Case and Evaluation Use Case Evaluation Model-Centered Evaluation Business-Centered Evaluation Monitoring Avoiding Common Pitfalls Change Management Project: Data Science Use Case In this course, participants choose a project task from a variety of options. The goal is to implement a data science model or system prototypically in a suitable development environment. The choice of approach, the implemented system or software, and the resulting performance in the task must be justified, explained, and documented in a project report.

Upcoming dates

• 2026-08-12T22:00:00.000Z · 2027-04-12T22:00:00.000Z · Karlsruhe, Baden · Blended Learning
• 2026-08-12T22:00:00.000Z · 2027-04-12T22:00:00.000Z · Duisburg · Blended Learning
• 2026-08-12T22:00:00.000Z · 2027-04-12T22:00:00.000Z · Leinfelden-Echterdingen · Blended Learning
• 2026-08-12T22:00:00.000Z · 2027-04-12T22:00:00.000Z · Nürnberg, Mittelfranken · Blended Learning
• 2026-08-12T22:00:00.000Z · 2027-04-12T22:00:00.000Z · München · Blended Learning