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2015/2016  KAN-CCMVV2556U  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
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Course Coordinator
    Ravi Vatrapu - Department of IT Mangement (ITM)
Kontaktinformation: https:/​/​e-campus.dk/​studium/​kontakt eller Contact information: https:/​/​e-campus.dk/​studium/​kontakt
Main academic disciplines
  • Information technology
  • Methodology and philosophy of science
  • Statistics and quantitative methods
Last updated on 21-04-2015
Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors: 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 costs to and 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. 40 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
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, methods, techniques, and tools of big data analytics from a business perspective.  Course contents will cover issues in and aspects of collecting, storing, manipulating, transforming, processing, analysing, visualizing, and reporting big data in order to create business value.  Course topics are listed below:

  • Big Data Analytics: Concepts, Lifecycle, Challenges, Opportunities, and Exemplary Cases
  • Data: Types, Structures & Tokens
  • Big Social Data Analytics: Predictive Analytics & Brand Loyalty Detection Algorithms
  • Visual Analytics: Dashboards
  • Computational Linguistics: Classification & Clustering
  • Computational Social Science: Social Set Analytics
  • Econometrics: Correlation, Regression, and Autometrics
  • Applications: Private and Public Sectors
  • Ethics & Privacy
Teaching methods
Lectures, Exercises, Demos, and Cases
Student workload
Lectures 33 hours
Exercises 19 hours
Preparation for Lectures and Exercises 55 hours
Project Work 70 hours
Project Report 40 hours
Expected literature

Selected Literature (Changes might occur)

Chapters 1 & 2 of Ohlhorst, F. J. (2012). Big Data Analytics: Turning Big Data Into Big Money. John Wiley & Sons.
Floyer, D. & Vellante, D. (2013). Enterprise Big Data.
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. (2011). Six provocations for big data.
Warden, P. (2011). Big Data Glossary. O'Reilly Media, Inc..
Wikipedia. (2014). Data Structure. http://en.wikipedia.org/wiki/Data_structure
Wikipedia. (2014). List of Data Structures. http://en.wikipedia.org/wiki/List_of_data_structures
On-line Library of Information Visualization Environments. (2014). http://lte-projects.umd.edu/Olive/
Vatrapu, R. (2013). Understanding Social Business. In K. B. Akhilesh (Ed.), Emerging Dimensions of Technology Management (pp. 147-158). New Delhi: Springer.
Robertson, S., Vatrapu, R., & Medina, R. (2010). Off the wall political discourse: Facebook use in the 2008 U.S. presidential election. Information Polity, 15(1,2), 11-31.
Hussain, A., Vatrapu, R., Hardt, D., & Jaffari, Z. (in press/2014).  Social Data Analytics Tool: A Demonstrative Case Study of Methodology and Software. In Gibson, R., et al (eds). Digital Methods, Palgrave Macmillan
Mukkamala, R., Hussain, A., & Vatrapu, R. (2014). Fuzzy-Set Based Sentiment Analysis of Big Social Data. Proceedings of IEEE EDOC 2014, Ulm, Germany.
Hussain, A., & Vatrapu, R. (2014). Social Data Analytics Tool: Social Data Analytics Tool: Design, Development and Demonstrative Case Studies.Proceedings of IEEE EDOC 2014, Ulm, Germany.
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, P. K. (2010). Measuring User Influence in Twitter: The Million Follower Fallacy. ICWSM, 10, 10-17.
Asur, S., & Huberman, B. A. (2010, August). Predicting the future with social media. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on (Vol. 1, pp. 492-499). IEEE.
Lassen, N., Madsen, R., & Vatrapu, R. (2014). Predicting iPhone Sales from iPhone Tweets. Proceedings of IEEE EDOC 2014, Ulm, Germany.
Romero, D. M., Galuba, W., Asur, S., & Huberman, B. A. (2011). Influence and passivity in social media. In Machine learning and knowledge discovery in databases (pp. 18-33). Springer Berlin Heidelberg.
Chapter 27 of Munzner, T. (2009). Visualization. Fundamentals of Graphics, Third Edition. AK Peters, 675-707.
Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Commun. ACM, 53(6), 59-67.
Executive Summary Thomas, J. J., & Cook, K. A. (Eds.). (2005). Illuminating the path: The research and development agenda for visual analytics. IEEE Computer Society Press.
Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S. (2012). Interactions with big data analytics. interactions, 19(3), 50-59.
Chris Zimmerman, Yuran Chen, Daniel Hardt, and Ravi Vatrapu. 2014. Marius, the giraffe: a comparative informatics case study of linguistic features of the social media discourse. In Proceedings of the 5th ACM international conference on Collaboration across boundaries: culture, distance & technology (CABS '14). ACM, New York, NY, USA, 131-140. DOI=10.1145/2631488.2631501 http://doi.acm.org/10.1145/2631488.2631501
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Danescu-Niculescu-Mizil, C., Kossinets, G., Kleinberg, J., & Lee, L. (2009). How opinions are received by online communities: a case study on amazon. com helpfulness votes. Proceedings of the 18th international conference on World wide web, 141-150.
Thomas, M., Pang, B., & Lee, L. (2006). Get out the vote: Determining support or opposition from Congressional floor-debate transcripts. Proceedings of the 2006 conference on empirical methods in natural language processing, 327-335.
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2), 267-307.
Constant, N., Davis, C., Potts, C., & Schwarz, F. (2009). The pragmatics of expressive content: Evidence from large corpora. Sprache und Datenverarbeitung, 33(1-2), 5-21.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.
Chapters 01, 07, 10 & 11 of Saxena, R. & Srinivasan, A. 2013. Business Analytics: A Practitioner's Guide, Springer New York.
Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD skills: new analysis practices for big data. Proceedings of the VLDB Endowment, 2(2), 1481-1492.
Davenport, T. H. (2006). Competing on analytics. harvard business review, 84(1), 98.
Part Two on Probability from Agresti, A., & Franklin, C. (2013). Statistics: The art and science of learning from data.
Hand, D. J. (1998). Data mining: statistics and more?. The American Statistician, 52(2), 112-118.
King, G. (1986). How not to lie with statistics: Avoiding common mistakes in quantitative political science. American Journal of Political Science, 666-687.
Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics, 33(5), 587-606.
Big Data Meets Big Data Analytics. SAS Whitepaper
Jacobs, A. (2009). The pathologies of big data. Communications of the ACM, 52(8), 36-44.
Herodotou, H., Lim, H., Luo, G., Borisov, N., Dong, L., Cetin, F. B., & Babu, S. (2011). Starfish: A Self-tuning System for Big Data Analytics. In CIDR (Vol. 11, pp. 261-272).
Chapters 1-2-3-4 of Zadrozny, P., & Kodali, R. (2013). Big Data and Splunk. In Big Data Analytics Using Splunk (pp. 1-7). Apress.
Chapter 4 of Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media.
Chapter 12 of Saxena, R. & Srinivasan, A. 2013. Business Analytics: A Practitioner's Guide, Springer New York.
Sheridan, J., & Tennison, J. (2010, April). Linking UK Government Data. In LDOW.
Slobogin, C. (2008). Government data mining and the fourth amendment. The University of Chicago Law Review, 317-341.
Davis, K. (2012). Ethics of Big Data. O'Reilly.
Craig, T., & Ludloff, M. E. (2011). Privacy and big data. O'Reilly Media, Inc..
Steinbrook, R. (2008). Personally controlled online health data-the next big thing in medical care?. New England Journal of Medicine, 358(16), 1653.
Last updated on 21-04-2015