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.

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

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). 

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. 

Grading is based on the following components:

• Final Project (40%)
• Final exam (30%)
• Project Outline (20%)
• Participation (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)

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

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

Upon request