Objectives and competences
The goal of the course is to introduce students to the field of data science and familiarize them with use cases, basic methods and tools for successful implementation of data science tasks and data-driven decision-making.
Content (Syllabus outline)
• Introduction: basic concepts, evolution of data science, data opportunities, value of data.
• Data science application areas and actual challenges, available techniques and tools.
• Data-driven decision-making: data, information, knowledge, decision-making, the process of using data to support decision-making.
• Life cycle of data analysis, planning the process of discovering patterns in data.
• Research and data visualization: exploratory data analysis, design and implementation of visualization.
• Basic data science techniques and tools: statistics, intelligent data analysis, machine learning, data mining.
• Communication of data and analysis results.
• Challenges and pitfalls in data science, ethical aspects and dilemmas.
• Selected applications of data science in various fields (biomedicine, financial technologies, environment, sports, human society, pharmacy, production).
Learning and teaching methods
• lectures,
• case studies,
• lab work,
• individual work.
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
On completion of this course the student will be able to
• understand the concept and importance of data science in a modern information society,
• demonstrate the knowledge and understanding of basic data science methods and models,
• participate in the information retrieval process for the needs of data-driven decision making,
• identify specific situations for the potential introduction of data science concepts.
Transferable/Key skills and other attributes:
• Communication skills: preparation and presentation of technical proposal, writing of seminary work.
• Problem solving: problem analysis with the use of data science concepts, intelligent data analysis.
Readings
• Foster Provost, Tom Fawcett: Data Science for Business – What you need to know about data mining and data-analytical thinking, 1st Edition, O’Reilly Media, 2013.
• J. Grus: Data Science from Scratch, 2nd edition, O’Reilly Media, 2019.
• D. Kos, V. Podgorelec (mentor): Uporaba orodja Orange za podatkovno rudarjenje, diplomsko delo, Univerza v Mariboru, Fakulteta za elektrotehniko, računalništvo in informatiko, 2017.
