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2023/2024  MA-MMBDV2057U  Business Data Analytics

English Title
Business Data Analytics

Course information

Language English
Course ECTS 6 ECTS
Type Elective
Level Part Time Master
Duration One Semester
Start time of the course Autumn, Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Min. participants 10
Max. participants 30
Study board
Study Board for Master i forretningsudvikling
Course coordinator
  • Raghava Rao Mukkamala - Department of Digitalisation (DIGI)
Main academic disciplines
  • Information technology
  • Organisation
  • Statistics and quantitative methods
Teaching methods
  • Face-to-face teaching
Last updated on 03-07-2023

Relevant links

Learning objectives
  • Identify and explain a business problem or opportunity with relevance to Business Data Analytics in your own organization and/or industry sector.
  • Demonstrate how the business data analytics course concepts, frameworks, methods, and tools can be used to analyze the business problem or opportunity.
  • Technically evaluate and critically reflect on the conceptual and practical implications of the business data analytics.
Course prerequisites
Students should have basic knowledge of management and experience with Excel.
The course target managers and specialists working with, or interested in working with, external and internal data for different business functions such as Customer Segmentation, Human Capital Analytics, Predictive Maintenance etc. The course is also open for professionals who would like to understand the power of analytics and get knowledge about how to use data for evidence-based management decisions.
Examination
Business Data Analytics:
Exam ECTS 6
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 pages
Assignment type Written assignment
Release of assignment An assigned subject is released in class
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter and Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content, structure and pedagogical approach

Most companies have accumulated a surfeit of business data: customer data, performance-related data, current and historic operational data, data from employees’ engagement surveys, etc. Yet, only few can make extensive use of data to drive evidence-based management decisions, support business development and transformation. Why so?

 

In addition to the need of owning data of a reasonable quality, developing a solid ground for evidence-based decisions requires having the right people, with the advanced analytical skills and scientific rigor in modeling and interpretation of the results.

 

The course provides participants with a deep understanding of the nature of big data and business analytics, and a practical toolkit on how to perform big data analysis. Our focus is on discovering different ways to generate business value from in-house and open big data sets for the purpose of increasing competitiveness in the global marketplace.

 

After having attended this course, you will gain knowledge about paradigms for generating competitive advantages and business value from new technologies. Furthermore, you will understand the logic behind business and big data analytics. The course also focuses on the importance of providing evidence to sustain managerial claims, applying an analytical process that covers all activities from problem formulation to result communication, and reflecting on and managing potential pitfalls.

 

You will be introduced to the Centre for Business Data Analytics framework for transforming big data sets into business assets by creating meaningful facts, actionable insights, valuable outcomes, and sustainable impacts. Building on the experience of Human Capital Analytics Group at CBS, you will learn about the different kinds of human capital analytics projects that can be carried out on corporate data. Projects that supports corporate transformation and business development by linking people and performance.

Description of the teaching methods
Blended Learning, Interactive lectures, Hands-on exercises, “Show-and-Tell” Demos, and In-Class Project Work.
Feedback during the teaching period
Feedback during class is possible.
Student workload
Preparation and exam 133 hours
Teaching 32 hours
Last updated on 03-07-2023