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2020/2021  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 22-06-2020

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, 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
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 for understanding automation of data processing and aggregation.
  • Tools: Tableau 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 exercise, Live Q&A sessions will be arranged.

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.

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
There will be 3 multiple choice quizzes during the course and those quizzes will be used to provide feedback regarding different topic covered in the course.
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 find the final literature on Canvas before they buy the books.

Last updated on 22-06-2020