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

Examination

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.