Data & Analytics
Niveau
Beginner
Learning outcomes of the courses/module
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.
Prerequisites for the course
None
Course content
- 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.
Recommended specialist literature
- 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.
Assessment methods and criteria
Exam
Language
English
Number of ECTS credits awarded
5
Share of e-learning in %
15
Semester hours per week
2.5
Planned teaching and learning method
Presentation, group work, discussion, exercises,
Semester/trimester in which the course/module is offered
1
Name of lecturer
Academic year
Key figure of the course/module
1_4
Type of course/module
integrated lecture
Type of course
Compulsory