Module group: foundational studies -> Specialized studies: Information Systems -> Course: Energy Efficient Systems

This module provides insights into the most important areas of computerized decision support based on system analysis, data analytics, operations research, and simulation for organizational needs. 
While predictive analytics, such as data mining, encompasses statistics-based models that make forecasts about future trends based on historical and current facts, prescriptive analytics, such as optimization, enable organizations to choose the best course of action. The combination of predictive and prescriptive analytics works toward the goal of an organization and helps to achieve both efficiency and effectiveness. Students will develop decision support systems using software R.

The objective of this course is to equip students with the fundamental concepts and methods of modern decision theory and practice by addressing the technical elements of the decision-making process trichotomy: intelligence, design and choice. The module foci are on predictive and prescriptive analytics taking organizations to a higher degree of intelligence and performance.

Workload: 180 hours

ECTS: 6

Lecture

The module covers the following topics:

- Predictive data analytics, including artificial intelligence, machine learning and data mining. These disciplines involve the procedures of finding and extracting the appropriate data to answer the question at hand, exploring the underlying processes, and then discovering patterns in the data using classification or segmentation.

- Prescriptive analytics, including multi-criteria decision analysis, optimization, information visualization, and groupware. This final and most difficult stage of decision analytics utilizes structured information obtained from the predictive phase, supplements it with semi-structured and unstructured information, such as expert judgments, to take advantage of the predictions and recommend optimal courses of managerial actions.

Students will acquire practical skills in modeling complex decisions and choosing appropriate solution/reporting tools. The project part of the module comprises analysis of a real-world case performed in small groups of three to four students.

Lecturer: Dr. Mariya Sodenkamp

Language: English

Type of teaching: Lecture (V)

Frequency: winter term/annual

Weekly hours: 2

Tutorial

The tutorial deepens the understanding of the content covered in the lecture and shows how to apply the methods in actual deployments.

Instructor: N. N.

Language: German / English, lecture notes predominantly in English

Type of teaching: Exercise (?)

Frequency: winter term/annual

Weekly hours: 2

Examination

The exam covers selected topics from both lectures and tutorials. The maximum score is 90 points, and the time for the exam is limited to 90 minutes. By completing and handing in course assignments during the semester, participants can earn up to 12 points. Points earned in course assignments only count towards the final grade if the exam is passed without considering the results from course assignments. Course assignments can take the form of short reports, presentations, or small programming tasks. At the beginning of the course, the dates for publishing and submitting the course assignments as well as the maximum points per assignment are communicated. A final grade of 1.0 can be achieved without points from course assignments.

Type: Written exam

Length: 90 minutes