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2015/2016  KAN-CINTO1011U  Big Data Management

English Title
Big Data Management

Course information
Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Full Degree Master
Duration One Semester
Start time of the course 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
  • Daniel Hardt - Department of IT Mangement (ITM)
Main academic disciplines
  • Information technology
  • Statistics and quantitative methods
Last updated on 12-08-2015
Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors: At the end of the course, students should be able to:
  • Demonstrate an in-depth understanding of contemporary discourses and trends in big data management
  • Identify persistent challenges to and emerging opportunities for value creation through big data management
  • Employ analytical tools and techniques, such as classification and regression, in crafting strategies for generating value from big data management.
Prerequisites for registering for the exam
Number of mandatory activities: 2
Compulsory assignments (assessed approved/not approved)
1. Submit Workplan for Final Project
2. Submit Interim Progress Report for Final Project
Examination
Big Data Management:
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance.
Individual or group exam Group exam, max. 3 students in the group
oral exam is individual
Size of written product Max. 20 pages
for 1 person max 10 pages; 2 people max 15 pages; 3 people max 20 pages
Assignment type Project
Duration
Written product to be submitted on specified date and time.
20 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-step scale
Examiner(s) Internal examiner and second internal examiner
Exam period Autumn
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content and structure

This course is designed to equip students with practical knowledge of tools and techniques for the purpose of analyzing large data sets. Students will also be exposed to different ways by which organizations leverage these tools and techniques to develop effective data management strategies for innovation and value creation. The course will further explore the transformative potential of big data management in different sectors of business and society. Learning activities include flipped classroom elements and an independently chosen project on big data management. The project will take the form of a business case analysis. Students will select a case organization of their choice and prescribe recommendations for transforming the organization through the utilization of tools and techniques for big data management.

The course will cover the following main topic areas:

  • Machine Learning tools and techniques, including classification and regression
  • Value creation through big data management
  • Data reduction and visualization
  • Transformative potential of big data analytics across various sectors, including Healthcare, Government and Retail 

 
Teaching methods
A mixture of lectures, case studies, group work, and practical exercises
Student workload
Lectures 30 hours
Class Preparation 117 hours
Exam and Preparation for Exam 60 hours
Expected literature

Provost, F. and Fawcett, T. Data Science for Business: What you need to know about Data Mining and Data-Analytic Thinking, 2013
 
[other readings]
 
●    LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., and Kruschwitz, N. “Big Data, Analytics and the Path from Insights to Value,” MIT Sloan Management Review (52:2), 2011, pp. 21-32.
 
●    Endert, A., Bradel, L. and North, C. “Beyond Control Panels: Direct Manipulation for Visual Analytics,” Computer Graphics and Applications, IEEE (33:4), 2013, pp. 6-13.
 
●    Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A. H. Big Data: The Next Frontier for Innovation, Competition, and Productivity, McKinsey Global Institute, May 2011, pp. 1-36.  [Available online at: http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation]
 
●    McAfee, A., and Brynjolfsson, E. “Big Data: The Management Revolution,” Harvard Business Review (90:10), 2012, pp. 59-68.
 
●    Shneiderman, B. “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations,” Proceedings of IEEE Symposium on Visual Languages – Boulder, CO, 1996, pp. 336-343.
 
●    Zoss, A. Introduction to Data Visualization, Duke University Libraries. [Available online at:http://guides.library.duke.edu/datavis]
Last updated on 12-08-2015