Objectives and competences
Use advanced tools for data preparation, analysis and visualisation.
Content (Syllabus outline)
Developing content and tools that offer data preparation, analysis and visualisation as a basis for independent work.
Focused on combining historic and current data to design predictions.
Learning and teaching methods
Seminar with practical demonstration and preparation for independent work
Intended learning outcomes - knowledge and understanding
1. An in-depth understanding of data visualisation and analytics, statistical analysis and data engineering, specifically in the business domain. (PILO 1a)
1. Understanding and ability to use massive data and techniques from data science, artificial intelligence and machine learning, specifically in the field of business. (PILO 3a)
2. discovering ways to create added value from the skills acquired. (PILO 2a)
2. Ability to data analysis software (PILO 3b)
3. Basic knowledge of data analysis programming (PILO 3a)
The PILO label (i.e., Programme Intended Learning Outcomes) defines the contribution of each listed intended learning outcome of a course towards achieving the general and/or subject-specific competencies or learning outcomes acquired through the programme.
Intended learning outcomes - transferable/key skills and other attributes
1. An in-depth understanding of data visualisation and analytics, statistical analysis and data engineering, specifically in the business domain. (PILO 1a)
1. Understanding and ability to use massive data and techniques from data science, artificial intelligence and machine learning, specifically in the field of business. (PILO 3a)
2. discovering ways to create added value from the skills acquired. (PILO 2a)
2. Ability to data analysis software (PILO 3b)
3. Basic knowledge of data analysis programming (PILO 3a)
The PILO label (i.e., Programme Intended Learning Outcomes) defines the contribution of each listed intended learning outcome of a course towards achieving the general and/or subject-specific competencies or learning outcomes acquired through the programme.
Readings
Bidgoli, H. (2021). Management information systems. Cengage Learning. ISBN - 978-0-357-41869-7 COBISS.SI-ID - 144315139
Chollet, Francois (2021) Deep Learning with Python, Second Edition ISBN 978-1617296864
Altair RapidMiner Academy (2023) https://academy.rapidminer.com/
Additional information on implementation and assessment - written exam or ongoing knowledge checks (50%)
- project (50%)
Written exam or ongoing knowledge checks - a written exam or two midterm tests covering all major topics. Students must achieve 56%.
Project - project report and presentation
