After this course, students:
1. Are able to translate a managerial problem into a research plan that includes suitable data and analysis choices
2. Are able to interpret the results of the research, and can translate these into managerial recommendations
3. Have become familiar with a variety of commonly encountered data types
4. Are able to perform advanced summative analysis on data encountered
5. Can identify suitable methods to analyse common data types encountered in firms
6. Are able to develop their own models based on the learned methods and the available data

With the increasing amount of data available within organizations, firms and managers are faced with the task of creating insights from these new and increasing sources of data. To make these insights accessible to end-users, firms have developed and used decision support systems (DSS) that aim to unlock data-driven insights for the use in day-to-day decision making. In general, DSS are software solutions that seek to combine data with analytical models in order to analyse these data and guide managerial decision making. This way, they create value for the firm. In this course we focus on developing DSS by combining data available to modern firms (i.e. both classical data as well as newer data sources such as online and text data) with analytical techniques to analyse these data. In particular the focus will lie on developing models appropriate for the data at hand, and interpreting the results from these analyses in order to base decisions on. As such, this course builds on and extends courses such as Business Analytics and Descriptive and Predictive Analytics.

A selection of articles/book chapters will be made available.

Experience in R, such as gained in the course Business Analytics. Prior experience in business modelling and statistics is highly recommended (e.g. obtained in courses such as Business Analytics and/or Descriptive and Predictive Analytics)