WIW64031 – Analytics for Data Driven Decisions

Module
Analytics for Data Driven Decisions
Analytics for Data Driven Decisions
Module number
WIW64031
Version: 1
Faculty
Business Administration
Level
Master
Duration
1 Semester
Semester
Summer semester
Module supervisor

Prof. Dr. Christoph Laroque
Christoph.Laroque(at)fh-zwickau.de

Prof. Dr. Susanne Bleich
Susanne.Bleich(at)fh-zwickau.de

Prof. Dr. Matthias Richter
M.Richter(at)fh-zwickau.de
Lecturer(s)

Prof. Dr. Christoph Laroque
Christoph.Laroque(at)fh-zwickau.de
Lecturer in: "Analytics for Data Driven Decisions"

Lecturer of the Kazakh-American Free University (KAFU)

Lecturer in: "Analytics for Data Driven Decisions"

Lecturer of the Armenian State University of Economics (ASUE)

Lecturer in: "Analytics for Data Driven Decisions"

Lecturer of the International Black Sea University (IBSU)

Lecturer in: "Analytics for Data Driven Decisions"

Lecturer of the Kyrgyz-German Institute of Applied Informatics (INAI.kg)

Lecturer in: "Analytics for Data Driven Decisions"
Course language(s)

English
in "Analytics for Data Driven Decisions"
ECTS credits

5.00 credits

Workload

150 hours

Courses

4.00 SCH (4.00 SCH Lecture with integrated exercise / seminar-lecture)
Self-study time

90.00 hours
90.00 hours Self-study - Analytics for Data Driven Decisions
Pre-examination(s)

siehe Hinweise
in "Analytics for Data Driven Decisions"
Examination(s)

schriftliche Prüfungsleistung
Module examination | Examination time: 90 min | Weighting: 100% | wird in englischer Sprache abgenommen
in "Analytics for Data Driven Decisions"
Media type
No information
Instruction content/structure

General teaching content:

Concepts and methods of computer-aided data analysis and their applications in the context of business administration, but especially

  • Data and its pre-processing
  • Visualization of information sets
  • Special methods of data analysis, e.g. Statistical Data Analysis, Six Sigma Methods, Clustering & Data Mining
  • Algorithms, methods and tools for mechanical learning
  • Case studies for data analysis in practical application
  • Application of BigData solutions

IBSU – Lecturer – Prof. Giorgi Ghlonti – special focus:

  • Data Mining (goal, characteristics, theory and practice, Data Warehouse Technology

KAFU – Lecturer – Prof. Aigerim Ismukhamedova – special focus:

  • Data and Business Analytics and Forecasting

ASUE – Lecturer – Prof. Yelena Manukyan – special focus:

  • Econometric and data analysis

INAI.kg – Lecturer – Prof. Aman Checheibaev – special focus:

  • Big Data Management (concept, characteristics, development lines, theory and practice, techniques, policies)
Qualification objectives

General learning objectives:

The students acquire skills for understanding and for the use of a wide variety of methods and concepts of the computer-aided data analysis and its correct, practical application. They are able to make observations and structure information sets in such a way that their essential patterns are recognizable and business decisions can be made.

The students are able to calculate appropriate key performance indicators, figures and methods for characterizing empirical data and also calculate it for very large amounts of data. They have mastered the essential concepts of graphical representations of data that can be used for further analyses with the help of statistical methods of machine learning by applying existing software solutions.

IBSU – special learning objective:

Students will learn how to use data mining concepts and techniques, letting them analyze large data sets and discover meaningful patterns and regularities.

KAFU – special learning objective:

You will learn how to reduce the customer's task to a formal statement of the machine learning problem and understand how to check the quality of the constructed model on historical data and in an online experiment.

ASUE – special learning objective:

Students will learn the main econometric analysis methods following the learning-by-doing approach of explaining econometrics through real-life examples.

INAI.kg – special learning objective:

Increase students’ knowledge of big data management policies, strategies and techniques, including data security, privacy, control and lifecycle management, offering modern open-source principles and architectures for successful big data management.

Social and Personal Skills

  • Interdisciplinary thinking at the intersection of Management, Business Information Systems and Computer Science
  • Training of analytical and logical thinking
  • Increasing self-awareness and self-confidence in newly gathered knowledge
  • Independently connecting the acquired knowledge with the subject PTI90290 Machine Learning and WIW32530 Advanced fields of Management
Special admission requirements

none
Recommended prerequisites
No information
Continuation options
No information
Literature
  • Thomas A. Runkler: Data Analytics - Models and Algorithms for Intelligent Data Analysis, Springer Fachmedien Wiesbaden 2016, DOI: 10.1007/978-3-658-14075-5
  • Steven Finlay: Predictive Analytics, Data Mining and Big Data - Myths, Misconceptions and Methods, Palgrave Macmillan, a division of Macmillan Publishers Limited 2014, DOI: 10.1057/9781137379283
  • Sayan Mukhopadhyay: Advanced Data Analytics Using Python - With Machine Learning, Deep Learning and NLP Examples, Apress, Berkeley, CA, DOI: 10.1007/978-1-4842-3450-1
  • OECD (2013), Exploring Data-Driven Innovation as a New Source of Growth: Mapping the Policy Issues Raised by „Big Data“, OECD Digital Economy Papers, No. 222, OECD Publishing
  • Glass, R.: The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors, and Boost Profits, John Wiley & Sons, 2015
  • Damodar Gujarati: Econometrics by Example, Second Edition, Palgrave, 2015
  • Peter Ghavami: Big Data Management: Data Governance Principles for Big Data Analytics De Gruyter; 1st edition, 2020 143 pages
  • Alhajj: Data Management and Analysis Springer; 1st ed. 2020 edition 2019 - 268 pages
  • Jiawei Han, Micheline Kamber and Jian Pei. Data Mining: Concepts and Techniques, 3rd ed. The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann Publishers, 2011
  • Larose C. D., Larose D. T.: Data Science Using Python and R. 2019. John Wiley & Sons, Inc.
  • Deep Learning. MIT Press book https://www.deeplearningbook.org/
  • Udacity course https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187
  • Stanford sophomore year in Networking, Natural Language Processing  http://cs224d.stanford.edu/
Notes

KAFU - Prerequisite(s) for examination admission:

  • At least 60% of homework exercises solved
  • all intermediate tests have been passed

IBSU – Lecturer – Prof. Giorgi Ghlonti

KAFU – Lecturer – Prof. Aigerim Ismukhamedova

ASUE – Lecturer – Prof. Yelena Manukyan

INAI.kg – Lecturer – Prof. Aman Checheibaev
Assignment to curriculum

035 Management and Information Technology - Master 2023 Vollzeit

Close detail view
© Modulux (Version 4.4)