IND310 Operational Optimization

The main course is two-part. Part one covers operational optimization using data science. Optimization deals with e.g. context understanding, method overview, process analyses, data information, models and simulation.

Part two is about master's preparations, where methodology and master's writing are taught, as well as the submission of a preliminary project for the master's thesis

Students must have a good understanding of methods, as well as an understanding of how to write a master's thesis. When the course has been completed, students should be able to manage operations, analyze and optimize processes. Emphasis will be placed on giving students knowledge about specific tools. There is a clear objective for the students to gain a practical insight into the topics covered in the course.

After completing the course

Our approach is digital decision support. IND310 will look at different contexts from the 5 sectors from the Industry Classification Benchmark (ICB): Health, Financial, Industry, Energy, Aquaculture. In order to make good decisions when working with data, we will first learn the approach to digital operations management through the CRISP-DM methodology: 1. Business understanding, 2. Data understanding 3. Data preparation 4. Modeling