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2019/2020  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 174
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
  • Blended learning
Last updated on 12-09-2019

Relevant links
Learning objectives
  • Understand and be good at explaining concepts in big data and big data analytics from managerial perspectives and consider ethical issues in big data
  • Understand unsupervised learning
  • Understand supervised learning concepts and methods presented in the course
  • Understand how to model and apply different algorithms and methods taught in the course
  • Understand core concepts in data mining and how to acquire and maintain competitive advantage with data mining
Course prerequisites
Introductory programming and basic statistics.
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

Project reports must be in the Project Report Template to be provided on the first day of classes and uploaded to Canvas:
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, storing, manipulating, transforming, processing, analysing, visualizing, and reporting big data in organisational settings to create business value. 

 

Course topics are listed below:

  • Foundations: Concepts, Lifecycle and Exemplary Cases
  • Data: Types, Structures & Tokens
  • Data Mining & Machine Learning: Algorithms & Tools
  • Visual Analytics: Dashboards
  • Text Analytics: Classification & Clustering
  • Predictive Analytics: Time-Series Econometrics
  • Computational Social Science: Social Set Analytics
  • Applications: Private and Public Sectors
  • Datafication: Security, Governanace, Regulation, Privacy & Ethics
Description of the teaching methods
Lectures
Voluntary Assignments
Tool Tutorials and Workshops
Project
Feedback during the teaching period
The teacher will give continous feedback during the course.
Student workload
Lectures 30 hours
Hands-on Exercises 30 hours
Tool Workshops: Preparation and Participation 30 hours
Individual Project: Work and Report 116 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 they buy the books.

 

F. Provost and T. Fawcett (2013), Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O’Reilly. 

 

Witten, Ian H., Eibe Frank, Mark A. Hall, and Christopher J. Pal. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, (3rd or 4th editions).

 

 
Last updated on 12-09-2019