Syllabus

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)

Prerequisites for participation and waiting lists

This course is part of the portfolio of elective courses of the Master of Science in Marketing program at WU. The course imparts in-depth knowledge in one or multiple selected specialized fields of marketing. The course builds on a strong foundation in marketing knowledge and skills which students acquire during the first year of WU's MSc Marketing program (this first year comprises 62.5 ECTS credits in total).

For MSc Marketing students, the successful completion of the following courses is a prerequisite for the admission to this elective course:

  • Management by Experiments (5 ECTS)
  • Marketing Analytics (7.5 ECTS)
  • Digital Marketing (5 ECTS)

Additionally, MSc Marketing students are prepared for this elective course by taking courses in the following fields during their first year:

  • Global Marketing Strategy (5 ECTS)
  • Qualitative Insights (5 ECTS)
  • Consumer Psychology (5 ECTS)
  • Consumer Value Management (5 ECTS)
  • Retailing & Sales (5 ECTS)
  • Business Modelling & Innovation (5 ECTS)

Moreover, this elective course builds on 45 ECTS credits in business administration which students had completed in order to be admitted to WU's MSc Marketing program.

Readings

1 Author: Intsitute for Retailing and Data Science
Title:

Slides and additional Information

https://wu-rds.github.io/storytelling/


Remarks: Course Homepage
Year: 2022
Content relevant for class examination: Yes
Recommendation: Essential reading for all students
2 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
Recommendation: Essential reading for all students
Type: Journal
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
Recommendation: Essential reading for all students
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
Recommendation: Essential reading for all students
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
Recommendation: Strongly recommended (but no absolute necessity for purchase)

Recommended previous knowledge and skills

Knowledge in R and statistical inference (to the level of e.g., Marketing Analytics)

Availability of lecturer(s)

Upon request

Last edited: 2022-11-29



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