Data & Analytics
Niveau
Beginners
Lernergebnisse der Lehrveranstaltungen/des Moduls
Upon completing this course, students will be able to:
• Understand Fundamental Statistical Principles:
• - Explain key concepts such as probability distributions, statistical inference, hypothesis testing, and descriptive statistics essential for data analysis.
• Apply Data Collection Techniques:
• - Design experiments and surveys with effective data collection techniques, utilizing sampling methods to collect data accurately while minimizing bias.
• Perform Exploratory data analysis:
• - Use exploratory data analysis (EDA) techniques to summarize the main characteristics of data through visual and quantitative methods, identifying patterns, trends, and anomalies.
• Utilize Mathematical Principles:
• - Apply basic mathematical principles, including algebra, geometry, and particularly integral calculation, to solve problems related to data analysis and interpretation
• - perform integral calculations for determining areas under curves, volumes, and other quantities essential for data modeling and analysis.
• Understand Fundamental Statistical Principles:
• - Explain key concepts such as probability distributions, statistical inference, hypothesis testing, and descriptive statistics essential for data analysis.
• Apply Data Collection Techniques:
• - Design experiments and surveys with effective data collection techniques, utilizing sampling methods to collect data accurately while minimizing bias.
• Perform Exploratory data analysis:
• - Use exploratory data analysis (EDA) techniques to summarize the main characteristics of data through visual and quantitative methods, identifying patterns, trends, and anomalies.
• Utilize Mathematical Principles:
• - Apply basic mathematical principles, including algebra, geometry, and particularly integral calculation, to solve problems related to data analysis and interpretation
• - perform integral calculations for determining areas under curves, volumes, and other quantities essential for data modeling and analysis.
Voraussetzungen der Lehrveranstaltung
None
Lehrinhalte
- Introduction to Data Analysis: Overview of data analysis, its importance in various fields, and an introduction to the data types (quantitative vs. qualitative).
- Mathematics for Data Analysis: Essential mathematical concepts, including algebra and geometry, and an introduction to calculus with a focus on integral calculation.
- Basic Statistical Principles: Introduction to descriptive statistics, probability theory, distributions, and the central limit theorem.
- Data Collection Methods: Exploration of various data collection techniques, sampling methods, and the design of experiments and surveys for accurate data gathering.
- Exploratory Data Analysis (EDA): Techniques for summarizing and visualizing data to identify patterns, outliers, and insights.
- Mathematics for Data Analysis: Essential mathematical concepts, including algebra and geometry, and an introduction to calculus with a focus on integral calculation.
- Basic Statistical Principles: Introduction to descriptive statistics, probability theory, distributions, and the central limit theorem.
- Data Collection Methods: Exploration of various data collection techniques, sampling methods, and the design of experiments and surveys for accurate data gathering.
- Exploratory Data Analysis (EDA): Techniques for summarizing and visualizing data to identify patterns, outliers, and insights.
Empfohlene Fachliteratur
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning: with Applications in R (2nd ed.). Springer. ISBN: 978-1071614174.
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. ISBN: 978-0387310732.
- Oppenheim, A. V., & Schafer, R. W. (2014). Discrete-Time Signal Processing (3rd ed.). Pearson. ISBN: 978-0131988422.
- Shumway, R. H., & Stoffer, D. S. (2017). Time Series Analysis and Its Applications: With R Examples (4th ed.). Springer. ISBN: 978-3319524511.
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. ISBN: 978-0387310732.
- Oppenheim, A. V., & Schafer, R. W. (2014). Discrete-Time Signal Processing (3rd ed.). Pearson. ISBN: 978-0131988422.
- Shumway, R. H., & Stoffer, D. S. (2017). Time Series Analysis and Its Applications: With R Examples (4th ed.). Springer. ISBN: 978-3319524511.
Bewertungsmethoden und -Kriterien
Portfolioprüfung
Unterrichtssprache
Englisch
Anzahl der zugewiesenen ECTS-Credits
3
Semesterwochenstunden (SWS)
Geplante Lehr- und Lernmethode
group work, discussion, exercises
Semester/Trisemester, In dem die Lehrveranstaltung/Das Modul Angeboten wird
1