Introduction to Big Data Analytics & Statistical Modeling
Niveau
Master
Learning outcomes of the courses/module
The participants:
• know fundamental concepts of big data, data architectures, and data processing.
• understand the entire data analysis process, from data preparation to modeling.
• apply methods of exploratory data analysis and statistical inference.
• analyze datasets in terms of quality, structure, and informative value.
• develop and interpret regression and classification models.
• critically evaluate models (e.g., validity, overfitting, generalizability).
• select appropriate methods and architectures for specific analytical problems.
• communicate analysis results in a target-oriented and data-driven manner.
• know fundamental concepts of big data, data architectures, and data processing.
• understand the entire data analysis process, from data preparation to modeling.
• apply methods of exploratory data analysis and statistical inference.
• analyze datasets in terms of quality, structure, and informative value.
• develop and interpret regression and classification models.
• critically evaluate models (e.g., validity, overfitting, generalizability).
• select appropriate methods and architectures for specific analytical problems.
• communicate analysis results in a target-oriented and data-driven manner.
Prerequisites for the course
None
Course content
• Introduction & Foundations (Big Data: 4Vs; overview of data science; data types; role of data in organizations)
• Data & Data Sources (structured vs. unstructured data; data quality; data integration; data preprocessing)
• Big Data Architectures (databases: relational vs. NoSQL; data warehouse vs. data lake; basic system architectures)
• Data Management & Processing (ETL/ELT; batch vs. stream processing; distributed systems; scalability – core principles)
• Data Analysis & Exploration (descriptive statistics; visualization; exploratory data analysis; data preparation in selected tools)
• Statistical Inference (sampling; uncertainty; estimation; missing data; bias and validity)
• Regression Models (linear and multiple regression; model assumptions; interpretation; model fit)
• Model Evaluation & Validation (train/test split; cross-validation; overfitting; generalizability)
• Advanced Models (logistic regression; classification; basic machine learning methods: trees, random forests)
• Text & Data Analysis (basics of NLP; simple text analysis; feature engineering)
• Communication & Visualization (presentation of results; storytelling with data; audience-oriented visualization)
• Data & Data Sources (structured vs. unstructured data; data quality; data integration; data preprocessing)
• Big Data Architectures (databases: relational vs. NoSQL; data warehouse vs. data lake; basic system architectures)
• Data Management & Processing (ETL/ELT; batch vs. stream processing; distributed systems; scalability – core principles)
• Data Analysis & Exploration (descriptive statistics; visualization; exploratory data analysis; data preparation in selected tools)
• Statistical Inference (sampling; uncertainty; estimation; missing data; bias and validity)
• Regression Models (linear and multiple regression; model assumptions; interpretation; model fit)
• Model Evaluation & Validation (train/test split; cross-validation; overfitting; generalizability)
• Advanced Models (logistic regression; classification; basic machine learning methods: trees, random forests)
• Text & Data Analysis (basics of NLP; simple text analysis; feature engineering)
• Communication & Visualization (presentation of results; storytelling with data; audience-oriented visualization)
Recommended specialist literature
• El-Kafrawy, P., & El-Amin, M. F. (2026). Mathematical modeling for big data analytics. Cambridge, MA: Elsevier.
• Engel, U., & Dahlhaus, L. (2026). Data science in social research: An applied guide to statistical and computational methods with R. Berlin/Boston: De Gruyter.
• Hummel, O., Kessel, M., Navarro, B., & Butscher, R. (2026). Data Science mit Big Data: Techniken, Werkzeuge und Algorithmen zur Analyse großer Datenmengen. München: Carl Hanser.
• Engel, U., & Dahlhaus, L. (2026). Data science in social research: An applied guide to statistical and computational methods with R. Berlin/Boston: De Gruyter.
• Hummel, O., Kessel, M., Navarro, B., & Butscher, R. (2026). Data Science mit Big Data: Techniken, Werkzeuge und Algorithmen zur Analyse großer Datenmengen. München: Carl Hanser.
Assessment methods and criteria
Exam
Homework
Homework
Language
German
Number of ECTS credits awarded
6
Semester hours per week
Planned teaching and learning method
The course is based on an e-learning concept that incorporates a didactically aligned combination of online and in-person phases. During the online phases, an inductive learning approach is applied, fostering the independent acquisition of knowledge and the completion of tasks. The in-person phases follow a deductive approach, in which learning processes are guided and deepened, and content is systematically delivered through lectures as well as practical examples.
Semester/trimester in which the course/module is offered
1