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2020/2021  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 Management, Society and Communication (MSC)
Main academic disciplines
  • Information technology
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 22-06-2020

Relevant links
Learning objectives
  • Understand and deploy machine learning techniques including classification, regression and clustering, as well as data visualisation.
  • Demonstrate an analytical understanding of data science methods, including the testing and assessment of data models.
  • Demonstrate an understanding of business and societal issues in the application of data science, including the expected value or profit for a given model.
Course prerequisites
Students should have the ability to work with computational models and quantitative methods.
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 Individual oral exam based on written group product
Number of people in the group 2-4
Size of written product Max. 15 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-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Autumn
Make-up exam/re-exam
Same examination form as the ordinary exam
Students can submit the same project or they can choose to submit a revised project.
Course content, structure and pedagogical approach

This course is designed to equip students with practical knowledge of tools and techniques for the purpose of analyzing large data sets and building predictive models. Students will study different ways to assess the potential cost and benefit of these models, and students will also study ways in which organizations leverage these tools and techniques to develop effective data management strategies for innovation and value creation. 

 

The course has a blended format, with most lectures presented online, together with associated online activities. In addition, there will be weekly hands-on lab sessions. The course includes an independently chosen project on big data management. The project will take the form of a business case analysis. Students will select a dataset, to which they apply data science techniques, building relevant models and assessing them from a business and data science perspective.

 

The course will cover the following main topic areas:

  • Machine Learning tools and techniques, including classification, regression, and clustering
  • Value creation through big data management
  • Model analysis and data visualization
Description of the teaching methods
A mixture of online lectures, other online activities such as quizzes, group work, and practical exercises in a hands-on session
Feedback during the teaching period
Students have hands-on exercises each week, where they receive in-person feedback from the teacher. They also receive weekly written feedback on their work. Mid-way through the course, they create a plan for their project, and they receive feedback from the professor on their plan. There are also weekly office hours.
Student workload
Lectures 20 hours
Hands-on Activities 20 hours
Class Preparation 106 hours
Exam and Preparation for Exam 60 hours
Total 206 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the literature on Canvas before they buy the books.

 

Provost, F. and Fawcett, T. Data Science for Business: What you need to know about Data Mining and Data-Analytic Thinking, 2013
 
Last updated on 22-06-2020