FHV Vorarlberg University
of Applied Sciences
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Information on individual educational components (ECTS-Course descriptions) per semester

Applied Statistics

Degree programme Business Informatics – Digital Transformation
Subject area Engineering & Technology
Type of degree Master
Part-time
Summer Semester 2024
Course unit title Applied Statistics
Course unit code 087421020301
Language of instruction German
Type of course unit (compulsory, optional) Compulsory
Teaching hours per week 2
Year of study 2024
Level of the course / module according to the curriculum
Number of ECTS credits allocated 3
Name of lecturer(s) Kathrin PLANKENSTEINER
Requirements and Prerequisites

Basics of business informatics, in particular teaching content on mathematics/statistics

Course content
  • Levels of measurement, EDA (uni- and multivariate descriptive statistics, Visualization)
  • continuous distributions: Normal distribution, Student-t-distribution
  • inferential statistics: estimator, confidence intervals, hypothesis and distribution testing
  • Linear regression
  • (time series analysis)
Learning outcomes

In a data-driven word, statistical methods are necessary to understand and adapt algorithms for knowledge generation and to evaluate results. The goal of the course is to familiarize students with basic statistical methods and tools for data-driven analysis.

The students

  • know measures of location, scale and correlation. They can calculate and interpret results.
  • can perform an EDA independently and interpret the results correctly.
  • understand the Bayes theorem and know how to apply it.
  • know continuous distributions and apply those for modeling data and corresponding questions.
  • can calculate confidence intervals and know how to interpret those.
  • can calculate hypothesis and distribution test results and know how to interpret those.
  • understand the concept of linear regression as well as know how to calculate and interpret coefficients and error terms.
Planned learning activities and teaching methods
  • online learning with videos
  • Input on selected topics on-site
  • Exercises on selected topics and presentation incl. discussion of approach and results (on-site)
Assessment methods and criteria
  • DataCamp classes (25%)
  • Quizzes (25%)
  • Report about EDA (50%)

For a positive grade, overall across all parts of the examination a minimum of 50% of the possible points must be achieved.

Comment

 

 

Recommended or required reading
  • Backhaus, Klaus u.a. (2016): Multivariate Analysemethoden: eine anwendungsorientierte Einführung. , überarbeitete und aktualisierte Auflage. Berlin Heidelberg: Springer Gabler.
  • Downey, Allen (2014): Think Stats: Exploratory Data Analysis. O’Reilly Media, Inc.
  • Haack, Bertil; Tippe, Ulrike; Stobernack, Michael; Wendler, Tilo (2017): Mathematik für Wirtschaftswissenschaftler: Intuitiv und praxisnah. Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-55175-8.
  • Tabachnick, Barbara G.; Fidell, Linda S.; Ullman, Jodie B. (2019): Using multivariate statistics. Seventh edition. New York, NY: Pearson.
  • Weitz, Edmund (2021): Konkrete Mathematik (nicht nur) für Informatiker: Mit vielen Grafiken und Algorithmen in Python. , überarb. u. erw. Aufl. 2021 edition. Berlin Heidelberg: Springer Spektrum.
  • Teschl, Gerald; Teschl, Susanne (2014): Mathematik für Informatiker Band 2: Analysis und Statistik. Springer, EXamen.Press. https://doi.org/10.1007/978-3-642-54274-9
Mode of delivery (face-to-face, distance learning)

On-site course (exercises and seminar-based discussion of content)