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2019/2020  KAN-CDASV1902U  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 BSc/MSc in Business Administration and Information Systems, MSc
Course coordinator
  • Abid Hussain - Department of Digitalisation
Main academic disciplines
  • Information technology
Teaching methods
  • Blended learning
Last updated on 27-06-2019

Relevant links

Learning objectives
The course has following learning objectives
  • Demonstrate data fetch from online data sharing WEB apis
  • Compare the standard data formats for data sharing across software platforms
  • Reflect on different data storage possibilities available for business data
  • Explain and demonstrate knowledge of data processing and data retrieval using multi dimentional cubes and structured query language
  • Substantiate expertise for using data analysis and reporing tool
  • Describe an understanding of complete end to end business data analysis process
  • Apply analytical skills for implementation of business data processing using Business Intelligence and Reporting tools
Course prerequisites
The are no pre-requisites
Business Data Processing and Business Intelligence:
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
Size of written product Max. 15 pages
The exam assignment can be handed in individually or in groups of two students.

Student who write the assignment in groups have to show what their individual contribution are, and in such a way that it is possible to make an individual assessment.
Assignment type Written assignment
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
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

At the end of the course the students will work on a mini project. The students need to document end to end data analysis process in form of a report, demonstrating their learning from the course contents.


Course content, structure and pedagogical approach

Data driven decision making is core of business processes in the current age. It has become ever more important for business students to get acquainted with end to end process for business data analytics. 


The aim of this course to provide knowledge about all processes of business data analytics that includes data collection, data storage, data processing and reporting. Student get hands on experience with data analysis process and learn the complete life cycle of analysis with examples and real data sets. Following topics are covered:


  • Introduction to data collection using WEB API : Learning how data can be retrieved from APIs and open WEB Api services
  • Introduction to data formats : Getting familiar with data formar used for data sharing on digital platforms
  • Data Storage models and databases 
  • Introduction to relational algebra to understand the theory behind the relation data storage systems
  • Structured Query Language (SQL) : How to use SQL for fetching, aggregating and filtering data.
  • Introduction to Multi Dimensional Cubes for understanding automation of data processing and aggregation.
  • Tools : Microsoft Sql Server Analysis Services

Tools: Microsoft Power BI for reporting

Description of the teaching methods
The course consists of 24 hours of lectures and 24 hours of exercises. These are held as a mixture of theoretical teaching, hands on demo and practical exercises. Students will be provided code snippets and step by step guides to supplement their technical learning.

The students will be working with real-life data sources. The students will be provided with access to the tools that are selected for data analysis learning in this course.

Teaching Materials:
Lecture slides
Scientific articles

The students will work on a mini project to demonstrate their learning in the final exam.
Feedback during the teaching period
The teacher and any teaching assistants provide feedback during workshop hours as well as electronically using Learn.
Student workload
Lectures 30 hours
Prepare to class 56 hours
Project work 100 hours
Exam and prepare 20 hours
Expected literature

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

Last updated on 27-06-2019