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2018/2019  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
Max. participants 400
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Course Coordinator
    Ravi Vatrapu - Department of Digitalisation
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
Teaching methods
  • Blended learning
Last updated on 25-05-2018

Relevant links
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 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
Course prerequisites
This is a course about DOING big data analytics NOT talking about it. As such the course requires an interest in and commitment to hands-on learning.

This course is a part of the minor in Data in Business

This course cannot be taken together with the course CCMVV2556U Big Data Analytics due to overlap
Examination
Big Data Analytics:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Group exam
Please note the rules in the Programme Regulations about identification of individual contributions.
Number of people in the group 2-4
Size of written product Max. 40 pages
The student can also choose to have an individual exam. The size of the written product is 15 pages for an individual exam.
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
* if the student fails the ordinary exam the course coordinator chooses whether the student will have to hand in a revised product for the re- take or a new project.
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:

  • Foundations: Concepts, Lifecycle, Challenges, Opportunities, and Exemplary Cases
  • Data: Types, Structures & Tokens
  • Data Mining and Machine Learning: Algorithms & Tools
  • Visual Analytics: Dashboards & Tools
  • Text Analytics: Classification & Clustering
  • Predictive Analytics: Correlation, Regression, and Autometrics
  • Computational Social Science: Social Set Analytics
  • Applications: Private and Public Sectors
  • Datafication: Security, Governance, Regulation, Privacy & Ethics
Description of the teaching methods
Lectures
Exercises
Demos
Tutorials
Cases
Feedback during the teaching period
The teacher will give continous feedback during the course.
Student workload
Lectures 33 hours
Exercises 19 hours
Tool Workshops 55 hours
Project Work 70 hours
Project Report 40 hours
Expected literature

Class #: Topic    Readings

 

Class #01: Philosophy:

Algorithms+DataStructures=Programs    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/​
Chapters 1-3 of Council, N. (2013). Frontiers in massive data analysis: The National Academies Press Washington, DC.
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. 

 

Class #02: Data Mining-1    Chapters 4-6 of Council, N. (2013). Frontiers in massive data analysis: The National Academies Press Washington, DC.
Chapters 1-2 of Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014). Mining of massive datasets: Cambridge University Press.

 

Class #03: Visual Analytics-1    Chapter 9 of Council, N. (2013). Frontiers in massive data analysis: The National Academies Press Washington, DC.
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.
Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Commun. ACM, 53(6), 59-67.

 

Class #04: Visual Analytics-2    Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S. (2012). Interactions with big data analytics. interactions, 19(3), 50-59.
Chapter 27 of Munzner, T. (2009). Visualization. Fundamentals of Graphics, Third Edition. AK Peters, 675-707.

 

Class #05: Text Analytics-1    Chapters 1-4 of Aggarwal, C. C., & Zhai, C. (2012). Mining text data: Springer Science & Business Media.
Chapter 1-3 of Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: " O'Reilly Media, Inc.

 

Class #06: Text Analytics-2    Chapter 10-11 of Aggarwal, C. C., & Zhai, C. (2012). Mining text data: Springer Science & Business Media.
Chapter 6-7 of Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: " O'Reilly Media, Inc.

 

Class #07: Predictive Analytics-1    Chapters 7-8 of Council, N. (2013). Frontiers in massive data analysis: The National Academies Press Washington, DC.
Chapters 1, 2, 4 & 5 of Hyndman, R. J., & Athanasopoulos, G. (2014). Forecasting: principles and practice: OTexts: https:/​/​www.otexts.org/​fpp/​ 

 

Class #08: Predictive Analytics-2    Chapters 4, 5, & 7 of Hyndman, R. J., & Athanasopoulos, G. (2014). Forecasting: principles and practice: OTexts: https:/​/​www.otexts.org/​fpp/​ 
Chapter 4 of Milhoj, A. (2013). Practical Time Series Analysis Using SAS: SAS Institute.

 

Class #09: Predictive Analytics-3    Chapter 8  of Hyndman, R. J., & Athanasopoulos, G. (2014). Forecasting: principles and practice: OTexts: https:/​/​www.otexts.org/​fpp/​ 
Chapter 7 of Milhoj, A. (2013). Practical Time Series Analysis Using SAS: SAS Institute.

 

Class #10: Data Mining-2    Chapters 3, 10-11 of Council, N. (2013). Frontiers in massive data analysis: The National Academies Press Washington, DC.
Chapters 3 & 6 of Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014). Mining of massive datasets: Cambridge University Press.

 

Class #11: Practical Applications    Chapter 12 of Saxena, R. & Srinivasan, A. 2013. Business Analytics: A Practitioner's Guide, Springer New York.
Chapters 8-9 of Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014). Mining of massive datasets: Cambridge University Press.
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.
Last updated on 25-05-2018