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2019/2020  DIP-DHDVV9904U  Big Data and Decision-Making

English Title
Big Data and Decision-Making

Course information
Language English
Course ECTS 5 ECTS
Type Elective
Level Graduate Diploma
Duration One Quarter
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for Graduate Diploma in Business Administration (part 2)
Course coordinator
  • Weifang Wu - Department of Digitalisation
Main academic disciplines
  • Information technology
  • Organisation
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 04-04-2019

Relevant links
Learning objectives
Students will be trained with knowledge, skills and competences in the following aspects:
  • Demonstrate understanding of principles of data-driven decision-making
  • Demonstrate understanding of the benefits and drawbacks of data-driven decision-making
  • Apply data analytics tools to different data sets to your organization’s data management challenges
  • Convert datasets to models through data analytics
  • Interpret analytical models to make better decisions
  • Identify the key challenges in data-driven decision-making
  • Critically assess the models and select the best one
Examination
Big Data and Decision Making:
Exam ECTS 5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance.
Individual or group exam Individual oral exam based on written group product
Number of people in the group 3-4
Size of written product Max. 10 pages
Assignment type Project
Duration
Written product to be submitted on specified date and time.
20 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Autumn and Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content, structure and pedagogical approach

This course will enhance the students' ability to extract the powerful insights they need to make smarter business decisions. Students will learn the theory and practice behind regressions, hypothesis testing, machine learning and other data analytics tools. Upon completing this course, the students will be able to:
• Assign decision-making tools that will boost their ability to make daily managerial decisions 
• Utilize various big data analytics tools as decision-making aids 
• Critically evaluate insights produced by big data analytics and adjust decision-making accordingly 

 

As reliance on data increases in organizations, the pressure on junior and mid-level managers to engage in data-driven decision-making grows and the ability to do so becomes ever more important. While decision-making in different domains (eg, marketing vs. finance) follow different logics, the basic principles of using analytics for data-driven decision-making remain similar across these domains. 

 

The course will provide a conceptual understanding of data-driven decision making and as well as hands-on experience with different tools for data analytics and decision-making (eg, regressions and machine learning). Students will acquire the theories, tools and strategies to answer questions such as:
• What is clustering and how to use it for marketing strategy?
• What is the best way to conduct hypothesis testing using my data?
• How to evaluate and select a model for decision making?
• How to interpret the business models?

 

Students will develop practical data analytics skills at working on real-world datasets as part of their final project.
 
Description of the teaching methods
Both face-to-face and online teaching will be conducted
Lecture and Workshops in which the students due hands-on work applying different data-analytics tools to project of own choice and relevance to specialization.
Feedback during the teaching period
Feedback will be given throughout the course.
Student workload
Lectures 24 hours
Hands-on Activities 8 hours
Class Preparation 72 hours
Exam and Preparation for Exam 34 hours
Last updated on 04-04-2019