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2023/2024  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
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 01-02-2023

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 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, data storage and data retrieval using relational database structure and structured query language
  • Demonstrate understanding for NoSQL databases
  • 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
  • Substantiate expertise for using data analysis and reporing tool
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 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
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 formats used for data sharing on digital platforms
  • Data Storage models and databases 
  • Introduction to relational databases and the structure and functions of Relation Database Management Systems
  • Introduction to NoSql Databases : Comparison with Relational Databases
  • Structured Query Language (SQL) : How to use SQL for fetching, aggregating and filtering data.
  • Introduction to Dataware house concepts and schemas.
  • Tools: Tableau or Power BI for reporting, RDBMS for Relation Databases and SQL, MONGO DB for NoSql, Scripting language or visual tool for WEB API  
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 practical exercises. Students will be provided code snippets and step by step guides to supplement their technical learning. To provide support for technical learning, Live Online Q&A sessions will be held.

The students will be working with real-life data sources. The students will be either provided with 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 and NO Sql databases.
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 relation with exam project c) In person feedback during online QA sessions.
Student workload
Lectures 30 hours
Prepare to class 56 hours
Project work 100 hours
Exam and prepare 20 hours
Online Live Q&A support sessions 10 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

Last updated on 01-02-2023