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2018/2019  BA-BHAAI1077U  Communicating Insight: Data Visualization with Statistical Programming

English Title
Communicating Insight: Data Visualization with Statistical Programming

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration Summer
Start time of the course Summer
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 120
Study board
Study Board for BSc in Economics and Business Administration
Course coordinator
  • Course instructor - Ginger Grant, PhD. Associate Dean, Applied Research and Innovation Humber Institute of Technology and Advanced Learning, ginger.grant@me.comAnton Grau Larsen, Assistant Prof., CBS, agl.ioa@cbs.dk
    Sven Bislev - Department of Management, Society and Communication (MSC)
In case of any academic questions related to the course, please contact the course instructor or ISUP academic director, Sven Bislev at sb.msc@cbs.dk
Main academic disciplines
  • Communication
  • Methodology and philosophy of science
  • Statistics and quantitative methods
Teaching methods
  • Face-to-face teaching
Last updated on 05/12/2018

Relevant links

Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:
  • Produce and present data visualizations that answers a relevant problem.
  • Evaluate and criticize visualizations according to the principles in the literature.
  • Design, program and execute reproducible data visualizations that live up to the principles discussed in the academic literature.
  • Program and run R scripts with data collection, transformation and plotting in a Markdown report.
Course prerequisites
Basic descriptive statistics.
Communicating Insight: Data Visualization with Statistical Programming:
Exam ECTS 7.5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 15 pages
Assignment type Project
Duration Written product to be submitted on specified date and time.
Grading scale 7-step scale
Examiner(s) One internal examiner
Exam period Summer, Ordinary exam: Home Assignment: 25/26 June - 29 July 2019. Please note that exam will start on the first teaching day and will run in parallel with the course.
Retake exam: Home Assignment: 72-hour home assignment: 8-11 October 2019 – for all ISUP courses simultaneously
3rd attempt (2nd retake) exam: 72-hour home assignment: 25-28 November 2019 – for all ISUP courses simultaneously

Exam schedules available on https:/​/​www.cbs.dk/​uddannelse/​international-summer-university-programme-isup/​courses-and-exams
Make-up exam/re-exam
Same examination form as the ordinary exam
Retake exam: 72-hour home project assignment, max. 10 pages, new exam question
Exam form for 3rd attempt (2nd retake): 72-hour home project assignment, max. 10 pages, new exam question
Course content and structure

Communicating complex data requires an understanding of the fundamental principles of graphical representation. The analyst needs to present data succinctly and without distortion and importantly, produce graphics in a transparent and reproducible process that is open to scrutiny.


This course will give the students the skills that are required to communicate data and statistical insights. These skills go beyond statistical literacy and introduces the principles behind sound data visualization. Students will learn and apply concepts and frameworks such as the grammar of graphics, reproducible analysis and statistical programming. In this course the students will learn R - an open source programming language that is a favorite of the statistical community.  


    1. Data in its various forms 
        1. Lecture: The relationship between data formats and analysis
        2. Exercise: Data types in R
        3. Online exercise: Rstudio and data types
    2. The phases of statistical programming
        1. Lecture: Coding, projects and data storage
        2. Exercise: Coding style and data storage
        3. Online exercise: Coding flow
    3. Tidy Data and web scraping
        1. Lecture: From API’s, web-scraping to tidy data
        2. Exercise: From Twitter and Wikipedia API’s to tidy data
        3. Online exercise: Web scraping
    4. Reshaping data
        1. Lecture: Long, wide and relational data
        2. Exercise: Reshaping
        3. Online exercise: Tidyverse 
    5. Functional programming
        1. Lecture: The principles of functional programming
        2. Exercise: Defining and using functions
        3. Online exercise: Functions
    6. Principles in the Grammar of Graphics
        1. Lecture: The basic principles of the grammar of graphics
        2. Exercise: Building simple plots in ggplot2
        3. Online exercise: Barcharts, scatter plots, correlation matrices

Feedback: A short - survey and group discussion

    7. Further on the Grammar of Graphics
        1. Lecture: Communicating with graphics
        2. Exercise: Advanced plots in ggplot2
        3. Online exercise: Networks, geographical maps, heatmaps, regressions
    8. Scales and presentation
        1. Lecture: Conveying a message with colours, sizes and scales 
        2. Exercise: Theming and adjusting
        3. Online exercise: Tweaking
    9. Reproducible analysis and reporting
        1. Lecture: Reproducible research and presentation technique
        2. Exercise: Graphical formats, knitr and Markdown
        3. Online exercise: Markdown
    10. Large scale graphical analysis
        1. Lecture: Working with big data
        2. Exercise: Plotting functions and reports
        3. Online exercise: File formats
    11. Quality assurances
        1. Lecture: Pilots and theming
        2. Exercise: Adjusting themes
        3. Online exercise: Themes


Description of the teaching methods
Lectures: The lectures deals with the principles behind the analysis and communication of data.

Exercises: Each exercise revolves around a small task that is solved via a programmed script. Each script produces a small graphical representation of data.

Online exercises: Small scripted exercises (in Swirl) or videos. The online exercises are small and can be solved in 15 minutes. They remind the students of what they have learned and they get to apply it in a different setting.
Feedback during the teaching period
Students answer a small survey during the lecture. We discuss the results.

All Home Project Assignments/mini projects are based upon a research question (problem formulation) formulated by the students individually, and must be handed in to the course instructor for his/her approval no later than 11 July 2019. The instructor must approve the research question (problem formulation) no later than 16 July 2019. The approval is a feedback to the student about the instructor's assessment of the problem's relevance and the possibilities of producing a good report.
Student workload
Preliminary assignment 20 hours
Classroom attendance 33 hours
Preparation 126 hours
Feedback activity 7 hours
Examination 20 hours
Further Information

Preliminary Assignment: To help students get maximum value from ISUP courses, instructors provide a reading or a small number of readings or video clips to be read or viewed before the start of classes with a related task scheduled for class 1 in order to 'jump-start' the learning process.


Course timetable is available on https://www.cbs.dk/uddannelse/international-summer-university-programme-isup/courses-and-exams


We reserve the right to cancel the course if we do not get enough applications. This will be communicated on https://www.cbs.dk/uddannelse/international-summer-university-programme-isup/courses-and-exams end February 2019 at the latest.


Expected literature

Mandatory readings:


Healy, Kieran. 2018. Data Visualization: A Practical Introduction. Princeton University Press.


Additional relevant readings:


Tufte, Edward R. 2006. Beautiful Evidence. Vol. 1. Graphics Press Cheshire, CT.
Matloff, Norman. 2011. The Art of R Programming: A Tour of Statistical Software Design. 1st ed. No Starch Press.

Last updated on 05/12/2018