Business Analytics (BA) is about exploring and analysing large amounts of data to gain insight into past business performance in order to guide future business planning. This course introduces a portfolio of advanced data-centric methods which cover the three main directions in BA: Descriptive (“what happened?”), predictive (“what will happen?”), and prescriptive (“what should happen?”). The methods will be applied to various business cases with aim to demonstrate how to extract business value from data, provide data-driven decision support along with effective data management principles. In this course, advanced machine learning and optimization techniques are used so understanding of data science, machine learning and optimization basics are required as well as a good level of programming skills is expected. En studerende, der fuldt ud har opfyldt kursets mål, vil kunne:

• Spot business opportunities related to effective data utilization
• Provide data-driven decision support for a specific range of business problems
• Select and apply appropriate machine learning and data management tools
• Conduct small-scale machine learning experiments and understand related scaling principles
• Provide data-driven support for online business and marketing
• Conduct basic analysis of information in a natural language form
• Conduct basic network analysis in a business context
• Provide statistical guidance for resource-demanding problems

The classes are taught in an interactive manner, with theoretical parts, intermingled with practical exercises. The practical exercises can be done using any programming language of choice, however, programming support will be provided for Python only.

The main topics covered in the course include data management; machine learning in research and production; modern trends in artificial intelligence in business context; spatio-temporal prediction models; recommender systems; web data analysis; network analysis; natural language processing basics; Bayesian optimisation and active learning. 25. april, 2019