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2022/2023  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 17-05-2022

Relevant links
Learning objectives
  • Understand the fundamental concepts in big data analytics and machine learning
  • Conduct independent desk-based research involving secondary data and apply machine learning techniques to investigate a business or societal problem
  • Apply data exploration and visualization to understand the related problem
  • Choose appropriate models of machine learning to investigate the problem and evaluate the performance
  • Demonstate the ability of using unsupervised learning methods and tools to discover patterns
  • Demonstate the ability of using supervised learning methods and tools for classification
Course prerequisites
Basic statistics and great interest in quantitatve analysis.
Programming skills are not required.
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
Description of the exam procedure

- Students will work on some specific topics with relevant datasets provided. There is also an option that students can choose their own topic and the data related. There will be four weeks for the students to do the project and submit the written product.

- Students who are doing the re-exam have to choose their own topic and the relevant data. The provided datasets on Canvas can not be used for the re-exam anymore.
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, models 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 videos & quizzes & assignments
Workshops: 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 a multiple choice quiz at CBS Canvas for each session.
- Feedback to hands-on exercises will be given collectively based on students' questions.
- For each session, discussion board on Canvas will be used for feedback to students regarding their questions.
- Q&A regarding the final project will be arranged in the final session as well as in the discussion board.
- Individual consultation can be arranged during consultation hours.
Student workload
Lectures 30 hours
Tool Tutorials and Workshops 20 hours
Lecture Preparation and Reading 60 hours
Tool Workshop Preparation 20 hours
Individual Exam Project: Work and Report 76 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 17-05-2022