ETN-Code: MML041
Titel der Veranstaltung: Data Science
Untertitel: Data Science
Art der Lehrveranstaltung: Vorlesung
Kreditpunkte: 3
Semester: WiSe 2024/25
Turnus: gemäß Curricula
Semesterwochenstunden: 2
Kursverantwortliche/r: HERBERGER Tim Alexander [1201800033]
Dozent/in: Farou Zakarya [1202400037]
Tarcsi Ádám [1202400036]
Organisationseinheit: Andrássy Universität Budapest
Ziele und Inhalt des Kurses: Students interested in practical data analysis and decision-making - No programming skills required!
Thema der einzelnen Lehreinheiten:
Course Description:
This practical workshop focuses on the basics of Data Science, on effectively utilizing data for analysis and drawing insights without the need for prior programming expertise Participants will learn to apply theoretical concepts through practical examples and gain an understanding of data visualization and research methodology as well. Within the course the participants are going to learn and use some of the most common tools for data analysis, like Power BI and Python.
Course outline:
1. Introduction to Data Science (2 hours)
2. Introduction to Machine Learning (2 hours)
2. Data Preparation and Cleaning (2 hours)
3. Basics of Python for data analysis (8 hours)
Course Methodology:
The course mainly relies on hands-on work, including group work among participants, analysis of case studies, and project-based learning. Through interactive demonstrations and practical examples, participants will be able to apply theoretical concepts to real-life situations.
Course supplements:
Regular practical assignments and projects allowing participants to apply theory to practice.
Empfohlene Literatur (für die Gesamtveranstaltung):
Recommended Reading:
- "Data Science for Business" by Foster Provost and Tom Fawcett
- "Storytelling with Data" by Cole Nussbaumer Knaflic
- "Data Visualization: A Practical Introduction" by Kieran Healy
Sprache der Lehrveranstaltung: Deutsch (ger)
Notenskala: Unterschrift
Form und Umfang der Leistungskontrolle:
Written exam for the theoretical part + Practical task/ project.
Prüfungsanmeldung: über das elektronische Studienverwaltungssystem
Anmerkungen:
Additional Notes:
By the end of the practical workshop, participants will confidently apply data analysis and visualization tools in decision-making on professional projects.