Syllabus

Title
2294 Business Analytics II
Instructors
Jana Hlavinova, Ph.D.
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/30/24 to 10/06/24
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Wednesday 10/30/24 08:00 AM - 11:00 AM TC.5.02
Wednesday 11/06/24 02:00 PM - 05:00 PM TC.4.18
Wednesday 11/13/24 08:00 AM - 11:00 AM TC.5.02
Wednesday 12/04/24 08:00 AM - 11:00 AM TC.5.02
Wednesday 12/11/24 08:00 AM - 11:00 AM TC.5.02
Wednesday 12/18/24 08:00 AM - 11:00 AM TC.5.02
Wednesday 01/15/25 08:00 AM - 11:00 AM TC.5.02
Wednesday 01/22/25 12:00 PM - 02:00 PM LC.-1.038
Contents

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 statistical/computational (data science) methods and, to see how these methods can be applied in practice, the course builds around a case study. 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)

The focus is on being able to choose an appropriate method based on the type of data and on the underlying research question, to apply the methods in R and to accordingly interpret their results.

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. R is continuously used both in class and in the home assignments.

Assessment
  • Home assignments 30 points (6 assignments, 5 points each - group assignments)
  • In-class assignments 30 points (to be solved in class, 6 assignments, 6 points each, 5 best scores are counted towards the grade)
  • 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%
Readings

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Last edited: 2024-06-24



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