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2020/2021  BA-BINTV1051U  Big Data Analytics for Managers

English Title
Big Data Analytics for Managers

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 150
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, BSc
Course coordinator
  • Weifang Wu - Department of Digitalisation
Main academic disciplines
  • Managerial economics
  • Information technology
  • Statistics and quantitative methods
Teaching methods
  • Online teaching
Last updated on 10-06-2020

Relevant links
Learning objectives
  • Understand concepts in big data analytics from managerial perspectives
  • Use unsupervised learning methods and tools
  • Use supervised learning methods and tools
  • Use tools to explore and visualize data
  • Understand how to choose models and evaluate the performance
Course prerequisites
Basic statistics, great interest in quantitatve analysis, and open-minded for a different mindset
Examination
Big Data Analytics for Managers:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 15 pages
Assignment type Project
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content, structure and pedagogical approach

This course is designed to provide knowledge of key concepts, methods, techniques, and tools of big data analytics from a managerial perspective. Course contents will cover issues in and aspects of collecting, processing, analyzing, visualizing, and reporting big data in organizational settings to create business value. The course will also expose students to practical tools and the use of tools in real cases.

 

Course topics are listed below:

  • Foundations: concepts, lifecycle and exemplary cases
  • Data and data strategy
  • Unsupervised machine learning and tools
  • Supervised machine learning and tools
  • Visual analytics and tools
  • Model evaluation
  • Applications and ethics
Description of the teaching methods
Lectures: pre-recorded and live student work & discussion feedback
Tools Workshops: pre-recorded and live student work & discussion feedback
Feedback during the teaching period
- Quizzes and feedback to quizzes will be provided during the course.The students have access to at least 4 multiple choice quizzes at CBS Canvas, which provides feedback to each answer option.

- Tutorials and feedback to hands-on exercises will be given in workshops. For each workshop, students will be guided by specific tutorials about how to use the tools. Besides, feedback will be given collectively based on students' questions.

- Discussion board will be used for students regarding their questions for the final project and feedback will be given collectively.
Student workload
Lectures 30 hours
Tool Tutorials and Workshops 20 hours
Lecture Preparation and Reading 30 hours
Tool Workshop Preparation 20 hours
Individual Exam Project: Work and Report 106 hours
Total 206 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before purchasing the books.

 

 
Last updated on 10-06-2020