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2022/2023  KAN-CDSCO1003U  Visual Analytics

English Title
Visual Analytics

Course information
Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Master of Science (MSc) in Business Administration and Data Science
Course coordinator
  • Sippo Rossi - Department of Digitalisation
Main academic disciplines
  • Information technology
Teaching methods
  • Face-to-face teaching
Last updated on 17-05-2022

Relevant links
Learning objectives
After completing the course, the students will be able to
  • Characterize the phenomena of visual analytics with application of fundamental concepts, techniques and methods.
  • Exhibit basic knowledge of data types, data structures and interaction with a relational database management system
  • Analyze and apply visual analytics techniques for big/business data sets in organizational contexts
  • Understand the linkages between business intelligence and visual analytics and the potential benefits for organizations
  • Apply analytical skills for implementation of business data processing using Business Intelligence and Reporting tools
  • Exhibit basic knowledge of securing or masking sensitive data while employing applying visual analytics
  • Exhibit deeper knowledge and understanding of the topics as part of the project and the report should reflect on critical awareness of the methodological choices with written skills to accepted academic standards
Course prerequisites
While it is not mandatory, students are recommended to brush up some basic data handling & analytical skills like using joins, unions, summarizing data, etc.
Prerequisites for registering for the exam (activities during the teaching period)
Number of compulsory activities which must be approved (see section 13 of the Programme Regulations): 2
Compulsory home assignments
The students have to get 2 out of 3 assignments approved.

Each assignment is 3-5 pages written in groups of 1-4 students. Some assignments may require submitting the scripts used to complete the assignment.

There will not be any extra attempts provided to the students before the ordinary exam. If a student cannot hand in due to documented illness, or if a student does not get the activity approved in spite of making a real attempt, then the student will be given one extra attempt before the re-exam. Before the re-exam, there will be one home assignment (max. 10 pages) which will cover 3 mandatory assignments.

The purpose of mandatory assignments is to help the students learn how to process data and create dashboards and thus prepare them for the final submission. The three assignments will cover (1) databases and SQL (2) Extract, Transform and Load (ETL) operations, and (3) visualising data and building dashboards.

The first assignment will be on database systems and SQL. Students will be asked to create ER models, relational schemas and answer questions related to developing and maintaining databases. The assignment will also include SQL programming.

In the second assignment, students will practice ETL operations and get hands-on experience in transforming and joining raw data using Python as well as optionally Alteryx.

In the third assignment students will practice building effective visualisations as well as learn how to build a dashboard. Although the exercises and lectures will demonstrate dashboarding with Tableau, students may use other dashboarding software to complete the assignment.
Examination
Visual Analytics:
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance, see also the rules about examination forms in the programme regulations.
Individual or group exam Individual oral exam based on written group product
Number of people in the group 2-4
Size of written product Max. 25 pages
The written product should be supported by a working dashboard.
Assignment type Project
Duration
Written product to be submitted on specified date and time.
20 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and external examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content, structure and pedagogical approach

The course provides knowledge of various concepts, techniques and methods related to data management, dashboarding and visualizations. Topics covered are:

 

  • Databases and SQL
  • Data Management: Extract, Transform, Load (ETL). Developing workflows for handling and tranforming data
  • Effective visualization of data
  • Creating and evaluating dashboards

 

The course provides the students with practical hands-on experience on data management, ETL and dashboarding. Although the lectures exercises will use specific tools and programs, students are allowed to use also other tools in the course project.

 

After completing the course, the students will be able to handle real-word big/business datasets and develop dashboards providing actionable insights to managers.
Description of the teaching methods
The course consists of lectures, exercises, and assignments. Each lecture is followed by an exercise session, and there will be a teaching assistant providing technical support for assignments and course projects.

The presented theories, concepts and methods should be applied in practice and exercise sessions. The students work the entire semester on a mini project displaying the understanding of the concepts presented in the lectures and exercises.
Feedback during the teaching period
Feedback on mandatory assignments will be provided.
Feedback is given during the course as described below:

1. We go through the assignment requirements in the exercise class so students can clarify initial doubts, if any. Then the assignment is published on Canvas. The course instructor will provide a written criteria on assessments.

2. Each group will get written feedback explaining strengths and weaknesses of their work as well as the correct answers to the assignment.

3. Some of the the best submissions will be discussed and presented in subsequent lectures. Students might be requested to present their work too.
Student workload
Lectures 24 hours
Exercises 24 hours
Prepare to class 48 hours
Project work & report 100 hours
Exam and preparation 10 hours
Total 206 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas. Access to the a free license to use an online copy of the book "Database Systems" is provided to students who sign-up during the first week of the course.

 

  • Jukic, N., Vrbsky, S., Nestorov, S., & Sharma, A. (2014). Database systems: Introduction to databases and data warehouses. Pearson. (Selected chapters)

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

  • Few, S. (2007). Data Visualization: Past, Present, and Future. IBM Cognos Innovation Center, perceptualedge.com/​articles/​Whitepapers/​Data_Visualization.pdf
Last updated on 17-05-2022