Study Guide

Objective

To equip students with theory and methods of decision-making in the industrial context. Corporate organizations' internal and external information flows are identified, analysed and evaluated to develop more robust decision-making processes. To provide app-roaches to both deal-with and benefit-from using big data. An introduction to tools used today for these purposes are examined. The course also deals with intellectual property and ethics regarding company information and data.

Content

- Decision-making theory in general with focus on decision- making based on quantitative data
- The company's internal information flow
- The company's external information flow: market,
business, customers, macro flows
- Data-driven business in technology and economics
- How to collect data, validate, describe, analyse and
interpret data
- Predictive variables and target variables - a challenging
assignment
- Descriptive, predictive and prescriptive models
- The added value of data and data analysis
- Security, ethics and boundaries in using available and collected data.

Qualifications

No prerequisitites

Assessment criteria, satisfactory (1)

Sufficient (1) and Satisfactory (2)
Participating in the contact lessons, returned tasks.

Assessment criteria, good (3)

Good (3) and Very good (4)
Active participating in the contact lessons, returned tasks with good outcomes.

Assessment criteria, excellent (5)

Excellent (5)
Very active participating in the contact lessons, returned tasks with excellent outcomes.

Assessment criteria, approved/failed

Evaluation:
Numerical for PASS from 1 to 5 and
0 for FAIL: No participating in the contact lessons, no returned tasks.

Materials

Literature:
To be informed later or please have a look at the course implementation