Objectives and competences
Students will understand the concepts of using predictive analytics in business decision processes.
They will comprehend the business predictive analytics methodology.
They will analyse the cases of business prediction analysis and will be capable of transferring the knowledge to solve their research questions.
Content (Syllabus outline)
• Business predictive analytics principles,
• Predictive analytics methodology,
• Business predictive analytics cases examples.
• Introducing predictive analytics into business environment
Learning and teaching methods
• research assignments,
• lectures,
• case analysis with computer exercises.
Intended learning outcomes - knowledge and understanding
Development of Knowledge and Understanding
In the field of business forecasting and the forecasting implementation control, and executing business prediction analysis. The learner
? Knowledge base: has great depth and systematic understanding of business predictive analytics
? Ethical issues: can analyse and manage the implications of ethical dilemmas of e-business transformation and work pro-actively with others to formulate solutions
? Disciplinary methodologies: has a comprehensive understanding of techniques / methodologies to conduct business research projects.
Cognitive and Intellectual Skills
The learner:
? Analysis: with critical awareness, can undertake analysis, managing complexity, incompleteness of data or contradictions in the area of business predictive analytics
? Synthesis: can synthesise new approaches, in a manner that can contribute to the development of methodology or understanding in that discipline or practice
? Evaluation: has a level of conceptual understanding and critical capacities that allows independent evaluation of research, advanced scholarship and methodologies. Can argue alternative approaches
? Application: can act independently and with originality in problem solving, is able to lead in planning and implementing tasks for business prediction analytics at a professional level.
Key / Transferable Skills
The learner:
? Group working: can lead groups and work effectively with group. Can clarify task, managing the capacities of group members, negotiating and handling conflict with confidence
? Learning resources: is able to use full range of learning resources
? Self-evaluation: is reflective on own and others’ functioning in order to improve business prediction analytics usage
? Management of information: can undertake innovative research tasks competently and independently
? Autonomy: is independent and self-critical as learner; guides and supports the learning of others and can manage own continuing professional development
? Communication: can communicate complex or contentious information clearly and effectively to specialists / non-specialists, understands lack of understanding in business prediction analytics by others. Can act as a recognised and effective consultant
? Problem solving: can continue own professional study independently, can make use of others professionally within / outside the business prediction analysis.
Practical Skills
The learner:
? Application of skills: can operate in complex and unpredictable / specialised contexts that may be at the forefront of knowledge. Has overview of the issues governing good practice in the field of business predictive analytics
? Autonomy in skill use: can act in a professional capacity for self / others, with responsibility and largely autonomously and with initiative in complex and unpredictable situations
? Technical expertise: has technical mastery, performs smoothly with precision and effectiveness; can adapt skills and design or develop new skills / procedures for new situations.
Readings
• James Taylor (2011). Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics.
• The R Foundation (2012). The R project, accessible on http://www.r-project.org/
• Charu C. Aggarwal (2016) Recommender Systems Springer, IBM T. J. Watson Research CenterYorktownUSA https://link.springer.com/book/10.1007%2F978-3-319-29659-3
• Bill Hibbard (2015) Ethical Artificial Intelligence freely available at https://arxiv.org/ftp/arxiv/papers/1411/1411.1373.pdfv
• Matthew A. North (2012) Data Mining for the Masses freely available at https://docs.rapidminer.com/downloads/DataMiningForTheMasses.pdf
• https://blogs.sap.com/2021/05/27/sap-hana-machine-learning-resources/
Additional information on implementation and assessment • seminar work 50
• oral exam 50
Student passes the exam, when each part of the examination is evaluated as positiv.
