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
Upon successful completion of the course, the student will be able to:
1. Identify and explain the challenges of data science, ranging from data security to market trend forecasting (PILO 1a, 2a).
2. Systematically enhance knowledge in the field of business analytics (PILO 2a, 2b).
3. Apply information within the decision-making process and explain its functioning (PILO 2a, 2b).
4. Identify suitable data sources in a selected case and evaluate the appropriateness of methods for their analysis (PILO 2c, 3b).
5. Critically analyze complex, incomplete, and conflicting perspectives on data science and clearly explain the results of the analysis (PILO 2b, 3a).
6. Search for and synthesize new information and data, and integrate them into the appropriate professional framework (PILO 3a).
7. Recognize ethical dilemmas and demonstrate professional responsibility in the field of data science (PILO 4a).
8. Critically evaluate the sustainability and societal 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
Upon successful completion of the course, the student will be able to:
1. Identify and explain the challenges of data science, ranging from data security to market trend forecasting (PILO 1a, 2a).
2. Systematically enhance knowledge in the field of business analytics (PILO 2a, 2b).
3. Apply information within the decision-making process and explain its functioning (PILO 2a, 2b).
4. Identify suitable data sources in a selected case and evaluate the appropriateness of methods for their analysis (PILO 2c, 3b).
5. Critically analyze complex, incomplete, and conflicting perspectives on data science and clearly explain the results of the analysis (PILO 2b, 3a).
6. Search for and synthesize new information and data, and integrate them into the appropriate professional framework (PILO 3a).
7. Recognize ethical dilemmas and demonstrate professional responsibility in the field of data science (PILO 4a).
8. Critically evaluate the sustainability and societal 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
Karimpour, A. (2020). Fundamentals of data science with MATLAB: Introduction to scientific computing, data analysis, and data visualization. A. Karimpour.
Dodatna literature / Additional:
Nabor aktualnih člankov s področja podatkovne znanosti./Selection of scientific papers in the area of data science.
Matworks: Data Science Tutorial, https://au.mathworks.com/videos/series/data-science-tutorial.html
Additional information on implementation and assessment Seminar paper 100%
Note:
Students may replace the seminar paper with a case study and oral presentation.
