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 techniques are used so understanding of data science and machine learning basics is required as well as a good level of programming skills is expected. En studerende, der fuldt ud har opfyldt kursets mål, vil kunne:

• Identify 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
• Quantify uncertainties in predictive modelling

The classes are taught in an interactive manner, with theoretical parts, intermingled with practical exercises. The practical exercises are done in Python.

The main topics covered in the course includes web data mining; natural language processing; recommender systems; network analysis; deep learning; spatio-temporal prediction models; ensemble models; survival analysis; prediction uncertainty and causality. Each module corresponds to its own recommended texts, that will be provided in the end of the lecture notes. The mandatory texts shall be publicly available online. 19. april, 2021