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[ 977PADCIADK22 ] KS Introduction to Analytics and Digital Transformation
Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M1 - Master's programme 1. year Business Administration Markus Sinnl 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Economic and Business Analytics 2023W
Objectives Students understand how digital technologies can be employed to generate business value. They are able to analyze business processes and have basic knowledge on how they are supported by technology in current organizations. They know how data is processed and managed in current organizations and how to extract information relevant for business decisions. They are able to apply basic analytics techniques using appropriate tools to support business decisions.
Subject Basic ideas and techniques from descriptive, predictive, prescriptive and autonomous analytics; overview of the steps of the accompanying transformation process which is needed to allow a company to successfully apply these analytical techniques in practice; case studies, practical applications.
Criteria for evaluation Written exam, homework exercises
Methods The course content is taught using blended learning methods with interactive elements to consolidate knowledge.
Language English
Study material F. Provost, T. Fawcett: Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, O'Reilly, current edition

T. H. Davenport, J. Harris: Competing on analytics - The new science of winning, Harvard Business Review Press, current edition

D. Bertimas, A. O’Hair, W. Pulleyblank: The Analytics Edge, Dynamic Ideas LLC, current edition

Other supplemental literature will be announced each semester.

Changing subject? No
On-site course
Maximum number of participants 100
Assignment procedure Assignment according to priority