Teaching & Example Course:
Data Analysis and Visualization

I am available to teach custom courses and workshops on topics such as data analysis, visualization, Python programming, scientific computing, and more. Below are the courses I have taught, and an example lesson program for an earth sciences-focused data analysis and visualization course using Pandas and Matplotlib.

I am currently providing courses in the context of my main affiliation, and you can contact me there.

Taught Courses and Workshops

Example Course Curriculum

Data Analysis and Visualization: Pandas and Matplotlib

Duration: April 24, 2025 - May 15, 2025 | Location: Leipzig, Germany (On-site)

This event has concluded! But you can contact me to book a similar course or request a custom curriculum for your group (n>5).

Event Page

Module 1: Data Analysis Basics
Introduction to data analysis concepts and workflows in Python. Understanding data types, structures, and basic analytical approaches to extract insights from data.
Topics: Python data structures, NumPy foundations, statistical measures, data cleaning techniques

Module 2: Pandas Fundamentals
Core pandas library concepts for data manipulation and analysis. Working with Series and DataFrame objects to prepare data for visualization and analysis.
Topics: DataFrame creation, indexing, selection, basic operations, handling missing data

Module 3: Pandas Time Series & Data Operations
Working with time series data using pandas. Techniques for temporal data manipulation, resampling, and trend analysis with practical applications using TERENO environmental data.
Topics: Time series indexing, resampling, rolling windows, seasonality analysis, trend detection

Module 4: Visualization Basics
Foundation principles of effective data visualization. Understanding the visualization pipeline and choosing appropriate visual representations for different data types and analysis goals.
Topics: Visualization theory, chart selection, color theory, perception principles, data-ink ratio

Module 5: Matplotlib: Beyond Presentation
Advanced matplotlib techniques focusing on publication-quality visualizations. Creating complex plots and customizing visualizations for scientific communication and analysis.
Topics: Figure composition, publication standards, statistical visualizations, custom styling, integration with pandas

Module 6: Applied Analysis with Environmental Data
Practical application of pandas and matplotlib to analyze environmental datasets. Focusing on TERENO data to uncover trends, correlations, and forecasting approaches for soil and climate data.
Topics: Correlation analysis, trend visualization, multivariate plotting, forecasting techniques

Bonus
Module 7: Geospatial Visualization in Python
Techniques for visualizing geospatial data in Python using libraries that integrate with pandas and matplotlib. Creating maps and geographic visualizations for environmental data.
Topics: GeoPandas, basemaps, choropleth maps, point patterns, spatial interpolation

Bonus
Module 8: 3D Visualization using VTK/Python
Introduction to 3D scientific visualization using VTK in Python. Rendering volumetric data and 3D surfaces for advanced scientific visualization needs.
Topics: VTK basics, volume rendering, isosurfaces, 3D plotting techniques, interactive visualization

Bonus
Module 9: Information Processing and Estimation
Statistical approaches for information extraction and estimation from data. Techniques for handling uncertainty and extracting reliable insights from noisy environmental datasets.
Topics: Statistical inference, confidence intervals, hypothesis testing, regression analysis

Bonus
Module 10: Classical Machine Learning Approaches
Introduction to machine learning techniques for data analysis. Using scikit-learn with pandas for predictive modeling and pattern recognition in environmental data.
Topics: Supervised learning, clustering, dimensionality reduction, model evaluation