1000 Business Analytics II
Jana Hlavinova, Ph.D.
Contact details
Weekly hours
Language of instruction
09/26/22 to 10/02/22
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Day Date Time Room
Thursday 11/03/22 03:00 PM - 06:00 PM TC.3.06
Thursday 11/10/22 03:00 PM - 06:00 PM TC.3.06
Thursday 11/17/22 03:00 PM - 06:00 PM TC.3.06
Thursday 12/01/22 03:00 PM - 06:00 PM TC.3.06
Thursday 12/15/22 03:00 PM - 06:00 PM TC.3.06
Thursday 12/22/22 03:00 PM - 06:00 PM TC.3.06
Thursday 01/12/23 03:00 PM - 06:00 PM TC.3.06
Thursday 01/19/23 03:00 PM - 06:00 PM LC.2.064 PC Raum

In this course, students will learn to apply theoretical methods, such as those introduced in Business Analytics I, to real data. The focus of the course will be on Data Science (statistical/computational) methods and, to see how these methods can be applied in practice, the course builds around a case study related to advertising. Faced with a real-world data set, students progress through various steps of data management and model design in order to arrive at a business decision, which is effectively supported by quantitative methods.

Topics include:

  1. Basic Data Handling and Summary Statistics
  2. Data Processing and Visualization
  3. Hypothesis Testing
  4. Regression Models (Linear and Logistic)
Learning outcomes

After completion of the course, students will be able to understand and apply the principles, methods and tools of business analytics to practical problems. This includes knowledge on:

  • Handling, visualizing and summarizing big data files in R
  • Formulating and testing hypothesis, and interpreting their results in a business context
  • Design, application and validation of appropriate regression models
Attendance requirements

Attendance requirement is met if a student is present for at least 80% of the lectures.

Teaching/learning method(s)

The course is taught using a combination of lectures, class discussions, assignments and practical applications of the tools and methods introduced in Business Analytics I.

  • Home assignments 30 points
  • In class assignments 30 points
  • Final Exam 40 points


If you fullfill the attendance requirements, the following grading scale will be applied

  • Excellent (1): 87.5% - 100.0%
  • Good (2): 75.0% - <87.5%
  • Satisfactory (3): 62.5% - <75.0%
  • Sufficient (4): 50.0% - <62.5%
  • Fail (5): <50.0%
1 Author: Jana Hlavinová

Lecture slides and notes.

Content relevant for class examination: Yes
Recommendation: Essential reading for all students
2 Author: Institute for Interactive Marketing and Social Media (WU Wien)

Business Analytics 2019 – integrated script for all topics (

Type: Script
Last edited: 2022-08-19