Data & Analytics
Niveau
Beginner
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, and 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, and 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
Exam
Unterrichtssprache
Englisch
Anzahl der zugewiesenen ECTS-Credits
5
Semesterwochenstunden (SWS)
Geplante Lehr- und Lernmethode
Presentation, group work, discussion, exercises,
Semester/Trisemester, In dem die Lehrveranstaltung/Das Modul Angeboten wird
1