ETN-Code: MML041

Titel der Veranstaltung: Data Science

Untertitel:

Art der Lehrveranstaltung: Vorlesung

Kreditpunkte: 3

Semester: SoSe 2023/24

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 Excel (advanced), Power BI, Python. The practical exercises are partly completed through self-study using the Datacamp platform.

 

Course Outline:

1. Introduction to Data Science (2 hours)

• What is Data Science, big data, data mining?
• Overview of basic concepts and the process, implement data science projects
• Formulating research questions and hypotheses
• Business Applications and Case Studies: using data for decision making.
• Introduction to AI

 

2. Data Preparation and Cleaning (4 hours) 

• Data collection methods in practice
• Importing and formatting data
• Data cleaning
• Data Analysis within Excel (Power Query, Power Pivot)
• Business Intelligence in practice – Power BI

 

3. Basics of Python for data analysis (8 hours)

• Basics of Python
• Pandas, numpy for data analysis
• Importance, objectives and tools of data visualization: Matplotlib, seaborn for data visualization
• Exploratory Data Analysis in practice based on a financial case study
• sklearn for supervised/ unsupervised models, data preprocessing, feature selection, and dimensionality reduction, models comparison

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: Englisch (eng)

Notenskala: Prüfung (fünfstufig)

Form und Umfang der Leistungskontrolle:

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.

Assessment:

- Capstone Project (individual / team)