Syllabus

Title
6157 Data-based Storytelling
Instructors
Daniel Winkler, MSc (WU)
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
02/16/22 to 02/20/22
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Monday 03/07/22 11:00 AM - 03:30 PM TC.4.04
Monday 03/14/22 11:00 AM - 03:30 PM TC.4.04
Monday 03/21/22 11:00 AM - 03:30 PM TC.4.04
Monday 03/28/22 11:00 AM - 03:30 PM TC.4.04
Monday 04/04/22 11:00 AM - 03:30 PM TC.3.06

Contents

Both local startups like Gurkerl and multinational corporations such as Amazon have invested heavily in their data science departments. They have amassed vast datasets of customer data and run machine learning algorithms around the clock to separate signals that provide valuable insights from the large amount of noise in those datasets. However, translating coefficients spat out by machine learning algorithms into executable business decisions is usually not straight forward. Data scientists and business executives use different jargon which leads to ineffective communication and businesses are increasingly looking for ways to put their data to better use. 

In this course students will learn about the tools necessary to fill the gap between data science and business decisions and become "Data Translators". They will gain insight on the interaction of data, narrative, and visualization to learn how to effectively communicate data-based insights to audiences unfamiliar with statistical and machine learning jargon.

To interpret and effectively present data, the R programming language extended by data wrangling and visualization packages will be used. Throughout the course we will work on a business case to gain practical experience in translating real-world data into managerial recommendations.

Learning outcomes

The aims of this course are to teach students the methods, principles, and theories of interpretation, visualization, and communication of data-based insights and to apply these to practical business settings. The objectives of the course are:

  • To become a translator between data scientists and managers
  • To learn how to interpret raw data in a business setting
  • To learn how to effectively communicate data-based insights using appropriate visualizations and presentation techniques
  • To train your ability to analyze and interpret business and market data using R, a leading software package for statistical data analysis

Attendance requirements

You need to attend at least 80% of all classes to pass the course. Any classes missed must be compensated with a written essay about the materials covered. It is the student's obligation to gather the material necessary. This applies both to in-person as well as online classes (should the latter be necessary). Attendance is mandatory in the first lecture as well as the exam (final lecture). 

Teaching/learning method(s)

The course is taught using a combination of interactive lectures, class discussions, case analyses, computer exercises, and student presentations. Theories will be applied to a real-world business case presented by the students. The goal is to provide an open learning environment that encourages trial and error, discussions, and the development of practical skills for data-driven businesses. The focus of the course will be on data visualization and the tools required to create interpretable and engaging charts for different audiences. 

To be prepared for class, you must work through the material assigned for the week and be ready to answer questions about it. During the live sessions in the classroom, we will focus on live problem-solving and work through applications related to the contents and clarify points that require further discussion. It is suggested to come with questions or comments about the material that you think might be interesting and helpful to the class. Peer feedback and class discussions will be a major part of the course. To be able to make valuable contributions to those discussions, preparation is essential. 

Assessment

Grading is based on the following components:

  • Group presentations of business case (40%)
  • Final exam (30%)
  • Participation in class discussion based on study material (either written or in-class; 20%)
  • Exercises (10%)

The following grading scheme is used:

< 60%                                fail (5)

60% bis 69,99%               sufficient (4)

70% bis 79,99%               satisfactory (3)

80% bis 89,99%               good (2)

>= 90%                             excellent (1)

Readings

1 Author: Mary Anderson
Title:

Lost in Data Translation

 


Publisher: American Marketing Association
Remarks: Reading for Week 1
Year: 2021
Content relevant for class examination: Yes
Content relevant for diploma examination: No
Recommendation: Essential reading for all students
Type: Journal
2 Author: Hadley Wickham and Garrett Grolemund
Title:

R for Data Science


Publisher: O'Reilly
Edition: 1
Remarks: Reading for Week 1 (Chapters 1-7)
Year: 2017
Content relevant for class examination: Yes
Content relevant for diploma examination: No
Recommendation: Essential reading for all students
Type: Book
3 Author: Scott Berinato
Title:

Data Science and the Art of Persuasion


Publisher: Harvard Business Review
Remarks: Reading for Week 2
Year: 2019
Content relevant for class examination: Yes
Content relevant for diploma examination: No
Recommendation: Essential reading for all students
Type: Journal
4 Author: Elena Kazakova
Title:

The Psychology behind Data Visualization Techniques


Publisher: Towards Data Science
Remarks: Reading for Week 3
Year: 2021
Content relevant for class examination: Yes
Content relevant for diploma examination: No
Recommendation: Essential reading for all students
Type: Journal
5 Author: Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen
Title:

ggplot2: Elegant Graphics for Data Analysis


Publisher: Springer
Edition: 3
Remarks: Additional reading for Week 1
Year: 2022
Content relevant for class examination: No
Content relevant for diploma examination: No
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Book

Recommended previous knowledge and skills

Any introductory statistics class

Availability of lecturer(s)

Upon request

Last edited: 2022-02-21



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