Syllabus

Title
1144 Business Analytics II
Instructors
Camilla Damian, MSc (WU)
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
09/27/21 to 10/03/21
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Wednesday 10/20/21 06:00 PM - 09:00 PM LC.2.064 Raiffeisen PC Raum
Wednesday 10/27/21 06:00 PM - 09:00 PM LC.2.064 Raiffeisen PC Raum
Wednesday 11/03/21 06:00 PM - 09:00 PM LC.2.064 Raiffeisen PC Raum
Wednesday 11/17/21 06:00 PM - 09:00 PM LC.2.064 Raiffeisen PC Raum
Wednesday 12/01/21 06:00 PM - 09:00 PM Online-Einheit
Wednesday 12/15/21 06:00 PM - 09:00 PM Online-Einheit
Wednesday 01/12/22 06:00 PM - 09:00 PM LC.2.064 Raiffeisen PC Raum
Wednesday 01/19/22 06:00 PM - 09:00 PM LC.2.064 Raiffeisen PC Raum

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 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)
  5. Optimization

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
  • Modern optimization techniques
  • If time allows: dealing with unbalanced data

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.

Assessment

  • 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%

Readings

1 Author: Camilla Damian
Title:

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)
Title:

Business Analytics 2019 – integrated script for all topics (https://imsmwu.github.io/BA2019/_book/)


Type: Script
Last edited: 2021-09-07



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