Objectives and competences
Students design project plan for data science project. Gather and prepare financial data from publicly available data warehouses. Are able to conduct the project by themselves and appropriately present the results, in the manner ready for a user.
Content (Syllabus outline)
1. Preparation and workflow of data science projects
2. Interpretation of results
3. Preparing and presenting the results for and to users
4. Time validity of performed projects
5. Professional ethics in data science
Learning and teaching methods
discussion; individual study; case studies;
Intended learning outcomes - knowledge and understanding
Students in this course:
1. Recognise the business needs for data science projects and differentiate and assess the quality of the data science project (PILO 2a, PILO 2b).
2. Gather and appropriately prepare financial data from publicly available data warehouses and critically evaluate implemented data science projects (PILO 2c).
3. Critically analyse complex, incomplete (incomplete) and contradictory areas of knowledge and understandably explain the results of critical analysis ( PILO 2c, PILO 3a)
4. Independently argue their findings in analytical work on a selected question or case study (PILO 3b, PILO 4B).
5. Are aware of their own ethical and professional responsibility in risk management (PILO 4a).
6. Critically assess the sustainable and social impact of the conducted data project example (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
Cognitive/Intellectual skills:
- critically evaluate data science projects
- understand background, methods and use of data science
- develop the ability to communicate professionally and critically.
Key/Transferable skills
- Are able to translate financial decision problems in into a decision.
- Ability to participate in the interdisciplinary and international team.
- They build professional ethics.
Practical skills:
- Are able to identify and appropriately design a data science project
- Are able to choose appropriate method for a data science project
- Are able to perform a data science project
- Can present a data science project
Readings
Nabor aktualnih člankov s področja podatkovne znanosti./Selection of scientific papers in the area of data science.
Martin T. Hagan, Howard B. Demuth, Mark H. Beale, Orlando De Jes?s, Neural Network Design (2nd Edition), ISBN-10: 0-9717321-1-6, ISBN-13: 978-0-9717321-1-7. Pridobljeno 3. maja 2023: https://hagan.okstate.edu/NNDesign.pdf.
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Additional information on implementation and assessment Project
Project - writing an individual project or case study and oral presentation
100%
