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2026/2027  KAN-CDSCV1005U  Business Data Processing and Business Intelligence

English Title
Business Data Processing and Business Intelligence

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 120
Study board
Study Board for Digitalisation, Technology and Communication
Programme Master of Science (MSc) in Business Administration and Data Science
Course coordinator
  • Abid Hussain - Department of Digitalisation (DIGI)
Main academic disciplines
  • Information technology
Teaching methods
  • Blended learning
Last updated on 19-01-2026

Relevant links
Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:
  • Demonstrate an understanding of how Web APIs operate and how data can be accessed, stored, and shared through them.
  • Identify and compare standard data formats used to support data exchange in business intelligence processes.
  • Evaluate different data storage and modeling approaches used for managing business data.
  • Demonstrate understanding of relational database structures and apply SQL to process, manage, and retrieve business data.
  • Describe an understanding of complete end to end business data analysis process
  • Demonstrate application of analytical skills to implement data processing tasks and develop insights with Business Intelligence and reporting tools.
  • Show competence in applying data analysis and reporting tools to prepare, explore, and present business data.
Course prerequisites
The are no pre-requisites however student should be able to install software and setup configurations following step by step guidelines
Examination
Business Data Processing and Business Intelligence:
Exam ECTS 7,5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Aids Limited aids, see the list below:
The student is allowed to bring
  • Any calculator
  • In Paper format: Books (including translation dictionaries), compendiums and notes
The student will have access to
  • Advanced IT application package
Make-up exam/re-exam
Same examination form as the ordinary exam
The number of registered candidates for the make-up examination/re-take examination may warrant that it most appropriately be held as an oral examination. The programme office will inform the students if the make-up examination/re-take examination instead is held as an oral examination including a second examiner or external examiner.
Description of the exam procedure

This is a four hours sit-in examination. Students will be provided with one or more datasets to be used as context for the examination.

 

The examination assesses students’ ability to apply the conceptual and practical knowledge gained throughout the course. Questions may require working directly with the provided data as well as addressing broader concepts and techniques covered in the course.

 

Students are expected to demonstrate their learning in alignment with the course learning objectives through appropriate analysis, reasoning, and application.
Course content, structure and pedagogical approach

Data-driven decision making is a core component of modern business practice. As a result, it is increasingly important for business students to understand the end-to-end process of business data analytics.
This course provides foundational knowledge of the major stages of business data analytics, including data collection, storage, processing, and reporting. Students gain hands-on experience with real datasets and learn the complete analysis lifecycle through practical examples. Topics include:

 

  • Introduction to Data Collection via Web APIs: How data is retrieved from APIs and open web services, and the role of API frameworks in digital echo systems. 
  • Data Formats: Overview of common data formats used for digital data exchange.
  • High level understanding Data Storage models for operational as well as analytical databases 
  • Data Storage Models: High-level understanding of storage approaches for operational and analytical systems.
  • Relational Databases: Structure and functions of relational database management systems.
  • NoSQL Databases: Brief introduction and comparison with relational databases.
    Structured Query Language (SQL): Using SQL for querying, aggregating, and filtering data. Using SQL as a quick reporting tool.
  • Data Warehouse Concepts: Introduction to data warehouse architectures and schema designs.

Tools: Tableau or Power BI for reporting; PostgreSQL for relational databases and SQL; MongoDB for NoSQL; and a scripting language or visual tool for interacting with Web APIs interfaces.
Research-based teaching
CBS’ programmes and teaching are research-based. The following types of research-based knowledge and research-like activities are included in this course:
Research-based knowledge
  • Classic and basic theory
  • New theory
  • Models
Research-like activities
  • Data collection
  • Analysis
  • Discussion, critical reflection, modelling
Description of the teaching methods
The course consists of 30 hours of online pre-recorded lectures. These are held as a mixture of theoretical teaching, hands on demo and step by step tutorials. Students will be provided code snippets and guides to supplement their technical learning.

The course will have 10 hours of on campus workshop sessions. The workshops will primarily be Q&A sessions to cover any queries that arise from the teaching material covered during the lectures. The on campus session will also provide in person support for any installation issues regarding tools required to cover each module in this course.

The students will be working with real-life data sources. The students will be either provided with academic license access to the tools or they will be asked to register for trial version of same tools that are selected for data analysis learning in this course.

Students will also experience how to register and gain access to publicly available data sets using techniques like Web Apis and storage of data will be demoed using relational database storage systems.
The students will work on a mini project to demonstrate their learning in the final exam.
Feedback during the teaching period
There will be three ways to provide feedback. a) Two online quizzes with multiple choice questions and implicit feedback with survey results. b) On demand Individual feedback about topics covered and in relation with exam project c) In person feedback during in person QA sessions.
Student workload
Lectures 30 hours
Prepare to class 56 hours
On Campus Q&A support sessions 10 hours
Project work 90 hours
Exam and prepare 20 hours
Total 206 hours
Expected literature

, The literature can be changed before the semester starts. Students are advised to check the syllabus on Canvas before buying any material.

 

The literature consists of practical hands on guides for each technology covered. 

There are readings associated with each topic. 

 

  1. Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: From big data to big impact. MIS quarterly, pp.1165-1188.

  2. Han, J., Pei, J. and Kamber, M., 2011. Data mining: concepts and techniques. Elsevier. Chapter 1 & Chapter 4

  3. Nurseitov, N., Paulson, M., Reynolds, R. and Izurieta, C., 2009. Comparison of JSON and XML data interchange formats: a case study. Caine, 9, pp.157- 162.

  4. De, B., 2017. API Management. In API Management (pp. 15-28). Apress, Berkeley, CA. Chapter 1 and Chapter 2

  5. MongoDb official documentation

  6. Silberschatz, A., Korth, H., Sudarshan, S. Database System Concepts. 7th Edition. McGraw-Hill Education. ISBN13: 9780078022159

  7. Fisher, D., DeLine, R., Czerwinski, M. and Drucker, S., 2012. Interactions with big data analytics. interactions, 19(3), pp.50-59.

  8. Knaflic, C. (2015). Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons, Chapter 1 – 8

  9. Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Commun. ACM, 53(6), 59-67

  10. Jukic N, Vrbsky S, Nestorov S & Sharma A (2020). Database Systems: Introduction to Databases and Data Warehouse, ISBN: 978-1-943153-68-8. Chapter 7,8,9 
Last updated on 19-01-2026