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Instructors: Jun. Prof. Dr. Rouven E. Haschka
Event type:
Seminar / exercise
Org-unit: Corporate Management & Economics
Displayed in timetable as:
Data Science
Hours per week:
3
Credits:
6,0
Location:
Campus der Zeppelin Universität
Language of instruction:
Englisch
Min. | Max. participants:
10 | 35
Priority scheme: Standard-Priorisierung
Course content:
Introduction to Data Science
Overview of Descriptive, Prescriptive, and Predictive Analytics
Review of R Programming: Basic concepts, functions, and applications
Fundamental Probability Concepts
Data Structures: Qualitative, Quantitative, Time Series, Panel, Cross-sectional
Data Acquisition, Preprocessing, and Exploratory Data Analysis
Data Mining Techniques
Webscraping Fundamentals
Relational Databases: Basics and Applications
Summarising data, Handling Missing Observations and Outliers
Data Visualization Methods
Classification
Logistic Regression
Discriminant Analysis
Kernel-based Methods
k-Nearest Neighbors
Tree-based Methods
Support Vector Machines
Resampling Methods
Cross-Validation
Bootstrap Techniques
Dimensionality Reduction and Regularization
Principal Component Analysis (PCA)
Partial Least Squares (PLS)
Ridge and Lasso Regression
Educational objective:
Upon completing this course, students will gain proficiency in:
Foundations of Data Science, Business Analytics, and Statistical Learning
Data Management
Statistical Analyses
Machine Learning Techniques
Data Visualization
Ethical and Privacy Considerations
Additionally, students will develop the following competencies:
Critical Thinking: Ability to analyze and evaluate information critically.
Teamwork and Communication: Skills for collaboration and communication in a team setting.
Application Readiness: Practical application of data science techniques to real-world scenarios.
Ethics and Responsibility: Understanding and practicing ethical considerations in data science.
Further information about the exams:
Midterm: 25%, Endterm: 75%
Mandatory literature:
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, by Foster Provost, and Tom Fawcett (2017).
Data Science Fundamentals and Practical Approaches: Understand Why Data Science Is the Next, by Gypsy Nandi, and Rupam Kumar Sharma (2020).
Data Science: Theory, Analysis and Applications, by Qurban A Memon, and Shakeel Ahmed Khoja (2019).
Data Science for Beginners: An Introduction to the Fundamentals of Data Analysis and Machine Learning, by Brian Murray (2023).
An Introduction to Statistical Learning: with Applications in R, by Daniela Witten, Trevor Hastie, and Robert Tibshirani (2021).
Practical Data Science with R, by Nina Zumel and John Mount (2019).
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, by Hadley Wickham and Mine Cetinkaya-rundel and Garrett Grolemund (2023)
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python by Andrew Bruce and Peter Bruce and Peter Gedeck (2020).
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