Objectives and competences
Students gain a strong foundation in the areas that support analytics and are able to use theoretical knowledge in data analytics practices executed in the business world. and recognise and understand professional ethics in data science.
Content (Syllabus outline)
1. Data science: introduction
2. Overview of key concepts: from data security do forecasting market trends
3. Analytical process: how data is created, stored, accessed
4. Value of data science for business decisions
5. Professional ethics in data science
Learning and teaching methods
lectures; case studies; discussion; individual study
Intended learning outcomes - knowledge and understanding
Students in this course:
1. Identify and understand the challenges of data science: from data security to forecasting market trends (PILO 1a, PILO 2a).
2. Systematically upgrade their knowledge in the field of business analysis (PILO 2a, PILO 2b).
3. Are able to use and understand information in the context of the decision-making process (PILO 2a, PILO 2b).
4. In the selected case, they demonstrate the ability to find suitable data bases and are able to judge the suitability of the methods for their analysis ( PILO 2c, PILO 3b).
5. Critically analyse complex, incomplete and contradictory views on data science and understandably explain the results of their critical analysis (PILO 2b, PILO 3a).
6. Acquire the ability to search for and synthesize new information and data, the ability to place them in an appropriate professional framework (PILO 3a).
7. Are aware of the ethical and professional responsibility of data science (PILO 4a).
8. Critically assess the sustainable and social impact of data science (PILO 4b).
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
Students in this course:
1. Identify and understand the challenges of data science: from data security to forecasting market trends (PILO 1a, PILO 2a).
2. Systematically upgrade their knowledge in the field of business analysis (PILO 2a, PILO 2b).
3. Are able to use and understand information in the context of the decision-making process (PILO 2a, PILO 2b).
4. In the selected case, they demonstrate the ability to find suitable data bases and are able to judge the suitability of the methods for their analysis ( PILO 2c, PILO 3b).
5. Critically analyse complex, incomplete and contradictory views on data science and understandably explain the results of their critical analysis (PILO 2b, PILO 3a).
6. Acquire the ability to search for and synthesize new information and data, the ability to place them in an appropriate professional framework (PILO 3a).
7. Are aware of the ethical and professional responsibility of data science (PILO 4a).
8. Critically assess the sustainable and social impact of data science (PILO 4b).
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
Nabor aktualnih člankov s področja podatkovne znanosti./Selection of scientific papers in the area of data science.
Arash Karimpour, Fundamentals of Data Science with MATLAB: Introduction to Scientific Computing, Data Analysis, and Data Visualization, 2020. ISBN: 978-1735241012.
Matworks: Data Science Tutorial, https://au.mathworks.com/videos/series/data-science-tutorial.html
Additional information on implementation and assessment Seminar work (100%)
Seminar work - writing an individual seminar paper or case study and oral presentation.
