Courses - Overview

Course Number | 82-021-QM03-H-507

Degree | Bachelor

Semester | Summer

Course Type | Lecture and exercise

Participation limit | None

Course Category | Mandatory for the Bachelor program in Business Administration

Contact Hours | 4 SWS

Number of Credits | 5 ECTS

Language | German

Chair | Statistics and Quantitative Methods

Lecturer | Prof. Dr. Ulrich Küsters and Assistants

Learning Outcomes

• Students in the field of business administration acquire the basic statistical methods and notions.
• The methodical skills are acquired within the scope of the lecture. Firstly, the statistical theory is taught in order to provide a solid methodical basis.
• Students apply techniques in probability theory and descriptive statistics on practical issues within the scope of the exercises.
• The self-centered working techniques enable students to develop the competence to handle statistical methods self-reliantly as well as to efficiently apply those methods in practice.    

Course Content

• Introduction
• Descriptive statistics (incl. indices und measures of concentration and inequality)
• Probability theory

• Introduction (incl. axiom system, conditional probability and Bayes’s theorem)
• Discrete univariate distributions
• Continuous univariate distributions
• Discrete multivariate distributions
• Continuous multivariate distributions
• Limit theorems (incl. Chebyshev's Inequality and Laws of Large Numbers)

Teaching Methods

• Lecture and exercise

Grading

• Final exam                                                                                            (100 %)

Assessment criteria in detail

• Written exam at the end of semester

Average Workload

28 h   = Time of attendance lecture

28 h   = Preparation and postprocessing lecture

28 h   = Time of attendance tutorial

28 h   = Preparation and postprocessing tutorial

38 h   = Exam preparation

150 h = Total workload

Previous Knowledge

• Mathematics for Business

Readings

• Küsters, Ulrich (2015): Statistik, Foliensatz, KUE/WFI, Ingolstadt.
• Bamberg, G., Baur, F. und Krapp, M. (2012): Statistik. 17. Auflage, Oldenbourg
• Bamberg, G., Baur, F. und Krapp, M. (2012): Statistik-Arbeitsbuch: Übungsaufgaben-Fallstudien-Lösungen. 9. Auflage, Oldenbourg
• Schira, Josef (2012): Statistische Methoden der VWL und BWL. 4. Auflage, Pearson Studium, München.
• Fahrmeir, L., Künstler, R., Pigeot, I. und Tutz, G. (2011): Statistik - Der Weg zur Datenanalyse.
  7. Auflage, Springer-Verlag, Berlin.
• Mosler, K. und Schmid, F. (2009): Beschreibende Statistik und Wirtschaftsstatistik. 4. Auflage, Springer-Verlag, Heidelberg.
• Mosler, K. und Schmid, F. (2011): Wahrscheinlichkeitsrechnung und schließende Statistik.
  4. Auflage, Springer-Verlag, Heidelberg.

The block course takes place for 6 hours each on Fridays and Saturdays at the beginning of the summer terms.

The exact dates will be announced in the KU Campus system.

Course Number | 82-021-IFM08-H-0507

Degree | Bachelor

Semester | Summer, Winter

Course Type | Integrated Lecture and Exercise

Contact Hours | 2 SWS

Participation limit | 19*

Course Category | Compulsory elective (Bachelor in Business Administration, interdisciplinary master in Mathematics with focus on Business Mathematics), course can be attended by Master students

Number of Credits | 5 ECTS

Language | English or German

Chair | Statistics and Quantitative Methods

Lecturer | Prof. Dr. Ulrich Küsters and/or Assistants

Learning Outcomes

• Students acquire both baseline information and knowledge of selected programming techniques by using the statistical software environment R.
• The statistical analysis of data using R enables students to appropriately treat, prepare and graphically display empirical data.
• By addressing problems in the broad field of business and economics (i.e. statistical hypothesis testing, linear regression etc.), students gain decision making skills enabling them to conduct an analysis using R in a self-directed and aim-oriented manner.

Course Content

• Basics
  • Objects and data structures in R und how to manage them
    • Vectors
    • Matrizes
    • Arrays
    • Lists
    • Data Frames
  • Logic and missing values
  • Constructs for program control
    • Conditional statements (if … else and the like)
    • Loops
  • Character strings
• Data input and output
  • Working with Excel-Data
  • Read and write R objects
• Details of the R language
  • Functions
  • S3-class objects
  • Lazy Evaluation
• Graphics with R
• Statistics mit R
  • Basic functions
  • Random numbers
  • Distributions and samples
  • Linear models

Teaching Methods

• Integrated Lecture and Exercise
• Programming projects

Grading

• Final exam                                                                                             (100 %)

Assessment criteria in detail

• Final exam: assessment of theoretical and technical aspects of the programming language respectively of the statistical topics covered in class.

Average Workload

28 h   = Time of attendance

28 h   = Preparation and post-processing

66 h   = Homework assignment/ Programming project

28 h   = Exam preparation

150 h = Total workload

Previous Knowledge

• Mathematics for Business
• Descriptive Statistics and Probability Theory
• Statistical Inference and Multivariate Statistics

Readings

• Ligges, U. (2008): Programmieren mit R, 3. Auflage, Springer.
• Matloff, N. (2011): The Art of R Programming, No Starch Press.
• Lafaye de Micheaux, P., Drouilhet, R., Liquet, B. (2013): The R software: fundamentals of programming and statistical analysis, Springer
• Venables, W. N., Ripley, B. D. (2002): Modern Applied Statistics with S. 4. Auflage, Springer.
• Rizzo, M.L. (2008): Statistical Computing with R, Chapman Hall.

*Limit due to capacity restriction in computer pools (special admission procedure).

Course Number | 88-021-QM04-H-0818

Degree | Master

Semester | Summer or Winter

Course Type | Lecture and Exercise

Participation limit | 19*

Course Category | Compulsory elective (Business Analytics)

Contact Hours | 4 SWS

Number of Credits | 5 ECTS

Language | German

Chair | Statistics and Quantitative Methods & BA and Business Informatics

Lecturer | Prof. Dr. Ulrich Küsters and Prof. Dr. Thomas Setzer

Learning Outcomes

• The students possess the methodical competence and the theoretical basis and can thus name and explain important statistical methods of data science relevant to practice.
• Students can apply and compute selected methods with the help of a statistical software environment such as R or Python and interpret their results.
• By means of the important conceptual and theoretical extensions of the methods of data science, students acquire the competences to make problem-oriented and context-related decisions.

Course Content

• Overview of Data Science Methods and Basic Procedures of Machine Learning
• Data rooms and data geometric basics of machine learning
• Bias-Variance Trade-off and Error Decomposition
• Methods of supervised learning and feature assessment
• Models for dimension reduction or feature reduction
• Outlook

Teaching Methods

• Lecture and Exercise
• If possible, participants should take a charged laptop with pre-installed software with them to the events. The software to be installed will be announced.

Grading

• Final exam                                                                                                           (100%)

Assessment criteria in detail

• Written exam at the end of semester

Average Workload

28 h   = Time of attendance lecture

28 h   = Preparation and postprocessing lecture

28 h   = Time of attendance tutorial

28 h   = Preparation and postprocessing tutorial

38 h   = Exam preparation

150 h = Total workload

Previous Knowledge

• Mathematics for Business
• Descriptive Statistics and Probability Theory
• Statistical Inference and Multivariate Statistics
• Basic knowledge of statistical programming software such as R or Python is recommended. Participation in the course Data Science Tools is ideal, but not mandatory.

Readings

Will be announced in the kick off meeting