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2021/2022  KAN-CDSCV1900U  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 150
Study board
Master of Science (MSc) in Business Administration and Data Science
Course coordinator
  • Irfan Kanat - Department of Digitalisation
Main academic disciplines
  • Information technology
  • Methodology and philosophy of science
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 04-02-2021

Relevant links

Learning objectives
After completing the course, students should be able to
  • Characterize the phenomena of Big Data, Big Data Analytics, and apply different concepts, methods, and tools for analysing big data in organisational/societal contexts.
  • Understand the linkages between business intelligence/analytics and the potential costs to and benefits for organisations.
  • Demonstrate the applicability of various analytical techniques and algorithms on the big/organisational/social/open datasets to derive critical insights.
  • Critically assess, reflect and present the findings of big data analytics in terms of meaningful facts, actionable insights and their impact on organisations and society.
  • Analyse and apply different visual analytics concepts and tools for big datasets.
  • Exhibit deeper knowledge and understanding of the topics as part of the project and the report should reflect on critical awareness of the methodological choices with written skills to accepted academic standards.
Course prerequisites
This course is a part of the minor in Data in Business.
The course has a highly practical and hands on approach to Data Science. If you prefer more theoretical courses this course may not be for you. Moreover, this is a fast-paced and intensive course comprising visual, predictive and text analytics modules. Therefore, the students are expected to have the knowledge and a background in quantitative methods, without which it would be difficult to follow the course content and analytical techniques and algorithms taught in the course. As such the course requires an interest in and commitment to hands-on learning.
Prerequisites for registering for the exam (activities during the teaching period)
Number of compulsory activities which must be approved (see section 13 of the Programme Regulations): 2
Compulsory home assignments
Each student has to get 2 out of 3 activities approved in order to qualify for the final exam.

There are three group reports of max. 5 pages written in groups of 2-4 students.

Each team will be provided with written feedback on the reports.

Each report forms the foundation of a part of the final report. This ensures the students will understand the expectations of the final before submission.

There will not be any extra attempts provided to the students before the ordinary exam. If a student cannot participate in the compulsory activities due to documented illness, or if a student does not have the activities approved in spite of making a real attempt, then the student will be given one extra attempt before the re-exam.
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-point grading 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, structure and pedagogical approach

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: Fundementals of machine learning, Supervised and Unsupervised algorithms
  • Visual Analytics: Visuvalizations techniques, dashboards & Tools
  • Text Analytics: keyword analysis, text classification (sentiment analysis) & Topic Modelling
  • Analytics: Correlation, Regression, and Machine Learning


Description of the teaching methods
Feedback during the teaching period
As part of the mandatory assignments, the students will take 2 multiple choice quizzes. The students will receive feedback on the quizzes on whether her/his chosen answer is wrong and a clue to where she/he can read up on the subject.
Student workload
Lectures & Exercises 30 hours
Self study 48 hours
E-learning 48 hours
Project Work 50 hours
Project Report 30 hours
Total Hours 206 hours
Expected literature

The expected literature might be changed before the semester starts. Students are advised to find the final literature on the Canvas before the start of the class.




You will find my lecture notes on canvas for each topic. My lecutre notes cover everything you need for my course.


IF you want to dig deeper into the topics covered, please find the further reading below.






  • Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. 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.


  • Jurafsky, D., & Martin, J. H. (2018). Naive Bayes and Sentiment Classification. Chapter 4 of Speech and language processing (3rd Edition)


  • Chapter 06 of Aggarwal, C. C., & Zhai, C. (2012). Mining text data: Springer Science & Business Media.


Research Papers:


  • Davenport, 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.


  • Singh, D., & Reddy, C. K. (2015). A survey on platforms for big data analytics. Journal of big data, 2(1), 8.


  • 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.


  • 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.


  • 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.





Last updated on 04-02-2021