Nowadays, companies have access to very extensive and ever-growing amounts of data - for example via social media and the Internet (e.g. online social networks, wikis, rating and review communities, discussion forums), but also in traditional databases (e.g. data warehouses, customer databases). The targeted and well-founded analysis of this data enables improved decision support and harbours great potential in a wide range of application areas (e.g. innovation management, product development, marketing, customer relationship management, internal knowledge management). In the module "Big Data Analytics - Methods and Applications" , the necessary fundamentals and methods are taught and applied to specific examples. Students who have successfully completed this module know the essential theoretical fundamentals, potential applications and risks of big data analytics and can explain them. They are familiar with various methods for analysing extensive amounts of structured and unstructured data (e.g. collaborative and content-based filtering in the field of recommender systems, neural networks in the field of text mining and recurrent neural networks in the field of smart vehicles) and are able to assess and apply them. In addition, they are able to use these methods to solve practical problems (e.g. analysing real data sets using Python), interpret the results and derive recommendations for action.