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2020/2021  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
  • Lester Allan Lasrado - Department of Digitalisation
Main academic disciplines
  • Information technology
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 10-06-2020

Relevant links
Learning objectives
  • Characterize the phenomena of visual analytics with application of fundamental concepts, techniques and methods of visual Analytics
  • 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
  • Summarize the application areas, trends, and challenges in visual analysis
  • Critically assess the ethical and legal issues in 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 s. 13 of the Programme Regulations): 3
Compulsory home assignments
The students have get 3 out of 5 assignments approved.
Each assignment is 1-3 pages written in groups of 1-4 students.

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 catch up with data analysis, dash-boarding and thus prepare them for the final submission. The data-sets for mandatory assignments will be provided by the teacher. The five assignments will cover (1) foundations of data analysis, (2) the concept of Extract, Load,Transform (ETL), (3) visualizing KPI's,Time series and visualizing temporal data , (4) Row Level Security and (5) finally evaluating visualizations and dashboards.
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.
Individual or group exam Individual oral exam based on written group product
Number of people in the group 2-3
Size of written product Max. 15 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:

 

  • Data Management: Concept of Extract, Transform, Load (ETL). Developing workflows for handling and tranforming raw data.
  • Relationship between data and visualization, Data Extraction, Environment, Display, Data Abstraction,Task Abstaraction, Manipulation of view.
  • Colours in Visualization: Optics, Resolution, & Display, Use of colour scales.
  • Visual Salience and Finding Information: Creating an effective dashboard.
  • Evaluation of Dashboards: Usability Inspection Methods

 

The course provides the students with practical hands-on experience on data transformations, data management and dashboarding. Tools deployed will be announced in the first lecture (some examples are Alteryx, Tablaue Prep and Desktop, MS SQL, Python).

 

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 in the entire semester on a mini project displaying the understanding of the concepts presented in the lectures and exercises. CBS Learn is used for sharing documents, slides, exercises etc. as well as for interactive lessons if applicable.

Due to the Corona Crisis, the first and last lecture will be on-campus. Rest of the lectures will be online. Similarly, the first three exercise sessions will be on-campus, while the rest will be online.
Feedback during the teaching period
Feedback on mandatory assignments will 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. Students will work In groups of 2-4 people. The group will hand in the analysis and/or dashboard supported by a short video explaining their work in any standard video format (screen recording with good audio quality is expected).

3. After the due date, a video will be posted on Canvas providing students with a generic feedback and expected answers.

4. Some of the the best assignments submissions will be discussed in subsequent lectures. Students might be requested to present their work too.

5. Each group will also get a written and/or video feedback (usually 3-5 minutes) explaining strengths and weaknesses of their work.
The feedback is provided within 2 to 3 weeks from date of submission.
Student workload
Lectures 24 hours
Exercises 24 hours
Prepare to class 48 hours
Project work & report 100 hours
Exam and prepare 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 before they buy the books.

 

  • Ware, C. (2013). Information visualization: perception for design (Third ed.): Elsevier. (Chapter 1 and 4)

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

  • Munzner, T (2014). Visualization analysis and design. CRC press. (Selected Chapters).

  • Loth, A. (2019). Visual Analytics with Tableau. John Wiley & Sons.

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

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

  • Few, S. (2007). Data Visualization: Past, Present, and Future. IBM Cognos Innovation Center, perceptualedge.com/​articles/​Whitepapers/​Data_Visualization.pdf
  • Nielsen, J. (1994) "Enhancing the explanatory power of usability heuristics." Proceedings of the SIGCHI conference on Human Factors in Computing Systems.
Last updated on 10-06-2020