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2014/2015  KAN-CINTV3000U  Big Data Analytics

English Title
Big Data Analytics

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Course period Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, MSc
Course coordinator
  • Course Coordinator
    Ravi Vatrapu - DIGI
Administrative contact person is Jeanette Hansen at ITM (jha.itm@cbs.dk).
Changes in schedule may occur.
Thursday 14.25-17.00, week 36-41,43-46
Main academic disciplines
  • Philosophy and philosophy of science
  • Information Systems
  • Organization
  • Statistics and mathematics
  • Methodology
Last updated on 09-04-2014
Learning objectives
After completing the course, students should be able to
  • Characterize the phenomena of Big Data and Big Data Analytics
  • Analyze and apply different visual analytics concepts and tools for a big data sets
  • Analyze and apply different concepts, methods, and tools for analyzing big data in organzizational contexts
  • Understand the linkages between business intelligence and business analytics and the potential benefits for organziations
  • Critically assess the ethical and legal issues in Big Data Analytics
Project Exam:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Group exam, max. 5 students in the group
Size of written product Max. 20 pages
Assignment type Project
Duration Written product to be submitted on specified date and time.
Grading scale 7-step scale
Examiner(s) Internal examiner and second internal examiner
Exam period December/January
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content and structure
This course is designed to provide knowledge of key concepts and methods of big data analyticsfrom an business perspective.  Course contents will cover issues in and aspects of manipulating, storing, and analysing big data in order  to create organizational value. Topics will include:

Introduction to Big Data
Introduction to Computational Social Science
Business Intelligence vs. Business Analytics
Visual Analytics
Methods and Tools
Data Mining for Managers
Social Data Analytics
Predictive Analytics
Ethical and Legal Issues
Applications to Private Sector
Applications to Public Sector
Teaching methods
Lectures, Exercises, Demos, and Cases
Student workload
Lectures 30 hours
Exercises 15 hours
Preparation for Lectures and Exercises 55 hours
Project Work 80 hours
Project Report 40 hours
Further Information
Changes in course schedule may occur
Thursday 14.25-17.00, week 36-41, 43-46
Expected literature
Bughin, J., Livingston, J., & Marwaha, S. (2011). Seizing the potential of 'big data'. Mckinsey Quarterly, (4), 103-109.

Davenport, T. H., Barth, P., & Bean, R. (2012). How 'Big Data' Is Different. (cover story). MIT Sloan Management Review, 54(1), 43-46.

Hsinchun, C., Chiang, R. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.

Noren, A. (ed.) (2011). Big Data Now. O’Reilly Media, Inc.
Last updated on 09-04-2014