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TH Köln

Master Digital Sciences

Documents for Study Program Accreditation

Module »Advanced Business Intelligence and Analytics« (ABIA)

Organizational Details

Responsible for the module
Prof. Dr. Hartmut Westenberger (Faculty F10)
Language
English
Offered in
Winter Semester (Duration 1 Semester)
Location
Campus Gummersbach, or remote
Number of participants
minimum 5, maximum 25
Precondition
none
Recommendation
database, programming, data warehouse and data mining knowledge on Bachelor's level
ECTS
6
Effort
Total effort 180h
Total contact time
90h (18h lecture / 18h seminar / 36h exercise / 18h practical)
Time for self-learning
90h (containing 90h self-organized project work)
Exam
Project work in conjunction with portfolio creation and expert talk
Competences taught by the module
Analyze Domains, Model Systems
General criteria covered by the module
Digitization, Transfer

Mapping to Focus Areas

Below, you find the module's mapping to the study program's focus areas. This is done as a contribution to all relevant focus areas (in ECTS, and content-wise). This is also relevant for setting the module in relation to other modules, and tells to what extent the module might be part of other study programs.

Focus Area ECTS (prop.) Module Contribution to Focus Area
Generating and Accessing Knowledge 4

Ability to design an enterprise infrastructure for Business Intelligence / Business Analytics; i.e. analytical data structures, algorithms and processes to deliver analytical services - how data can be transformed to value-adding insights to the current business by classical means and how predictive means can improve upcomimg business decisions.
Empowering Business 2

Analyzing how data can foster value-adding insights to the current business by classical means and how predictive means can improve upcomimg business decisions.

Learning Outcome

  • Enabling students to design and implement a Business Intelligence and Business Analytics infrastructure so as to support management decision
  • by structuring customers‘ requirements, analyzing data source quality and identifying appropriate data structures and algorithms
  • they will become able to design an appropriate infrastructure. They plan the staging of raw data to analytical data and assess the applicability of classical and modern techniques delivered by common BI/BA platforms.
  • Based on these skills they will be able to build up an appropriate decision support infrastructure to improve decision processes and to maximize enterprise profits.

Module Content

  1. Classification of decision support
  2. Methodology Reference models for BI/BA infrastructure development
  3. Data Preparation for classical and advanced analytics
  4. Data structures for management support (Data vault, Multi Dimensional, No-SQL)
  5. Applicability of advanced algorithms

Forms of Teaching and Learning

  • Flipped classroom
  • Exercises + team work
  • hands-on-workshop on ETL tools

Learning Material Provided by Lecturer

  • Software tools for
  • … multidimensional modeling
  • … data transformation
  • … report generation
  • … data Mining

Literature

  • Giles, J.: Elephant in the Fridge. Guided steps to data vault success through building business-centered models. Technics Publications, 2019
  • Hultgren, H.: Modeling the Agile Data Warehouse with Data Vault. Brighton Hamilton, 2012.
  • Kimball R.: The Data Warehouse Lifecycle Toolkit. John Wiley & Sons. 2008
  • Linstedt, D.; Olschimke, M.: Building a scalable data warehouse with data vault 2.0. Amsterdam, Netherlands: Morgan Kaufmann, 2016.
  • further sources to follow