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2016/2017  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
Min. participants 25
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, BSc
Course coordinator
  • 40
    Benjamin Flesch - Department of IT Management (ITM)
  • 40
    Niels Buus Lassen - Department of IT Management (ITM)
  • 20
    Ravi Vatrapu - Department of IT Management (ITM)
Niels Buus Lassen and Benjamin Flesh will teach the majority of the course
Main academic disciplines
  • Managerial economics
  • Information technology
  • Statistics and quantitative methods
Last updated on 13-04-2016
Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:
  • Characterize the phenomena of Big Data and Big Data Analytics from a managerial perspective
  • Analyze and apply different visual analytics concepts and tools for big data sets
  • Familiarity with the different concepts, methods, and tools for analyzing big data in an organizational context
  • Understand the linkages between business intelligence and business analytics and the potential costs and benefits of integrating big data analytics in business processes.
  • Critically assess the ethical and legal issues in Big Data Analytics
Course prerequisites
None
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. 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 Winter and 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 LEARN:
Course content and structure

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
Teaching methods
Online Screen Recorded Lectures
Online Assignments
Tool Tutorials and Workshops
Technical Support
Student workload
Online Lectures 30 hours
Online Assignments 30 hours
Tool Workshops: Preparation and Participation 30 hours
Individual Project: Work and Report 125 hours
Expected literature

Selected Book Chapters:

Council, N. (2013). Frontiers in massive data analysis: The National Academies Press Washington, DC.

 

Thomas, J. J., & Cook, K. A. (Eds.). (2005). Illuminating the path: The research and development agenda for visual analytics. IEEE Computer Society Press.

 

Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014). Mining of massive datasets: Cambridge University Press.

 

Munzner, T. (2009). Visualization. Fundamentals of Graphics, Third Edition. AK Peters, 675-707.

 

Aggarwal, C. C., & Zhai, C. (2012). Mining text data: Springer Science & Business Media.Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: " O'Reilly Media, Inc.

 

Hyndman, R. J., & Athanasopoulos, G. (2014). Forecasting: principles and practice: OTexts: https://www.otexts.org/fpp/

 

Milhoj, A. (2013). Practical Time Series Analysis Using SAS: SAS Institute.

 

Saxena, R. & Srinivasan, A. 2013. Business Analytics: A Practitioner's Guide, Springer New York.

 

Articles

Daveport, T. (2014). 10 Kinds of Stories to Tell with Data. Blog post at Harvard Business Review. http://blogs.hbr.org/2014/05/10-kinds-of-stories-to-tell-with-data/

 

Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679.

 

Tufekci, Z. (2014). Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls. Proceedings of the 8th International Aaai Conference on Weblogs and Social Media(ICWSM) 2014, Ann Arbor, USA, June 2-4, 2014.

 

Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Commun. ACM, 53(6), 59-67.

 

Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S. (2012). Interactions with big data analytics. interactions, 19(3), 50-59.

 

Slobogin, C. (2008). Government data mining and the fourth amendment. The University of Chicago Law Review, 317-341.
Last updated on 13-04-2016