Applied Data Management and Analysis

The exercise will practice the reuse of research data with Python: loading data into data frames, getting an overview on the data, data cleaning, exploration and preparation. It will teach: 1. basic and advanced statistics with Python using PANGAEA data. 2. Distribution analysis, missing data treatment, outlier detection. 3. Applied data analytics, regression analysis, trends, smoothing. Basic plotting of data using Python.

Students are introduced to methods to manage, submit and archive research data in relevant information systems.
Students are introduced to methods for data handling, data exploration, data analysis and statistics with Python.

Active participation
Protocol

protocol

Literature will be provided

1. SWS: Refreshing basic Python skills
2. SWS: Code versioning with Git and documentation with Jupyter Notebooks
3. SWS: Finding and downloading research data, data formats and viewers
4. SWS: Loading, merging and viewing research data for analysis

5. SWS: Querying and filtering data, grouping and aggregations
6. SWS: Cleaning and harmonising datasets
7. SWS: Data profiling and summary statistics
8. SWS: Basic data plotting and visual data exploration
9. SWS: Univariate analysis (distribution, confidence, variance etc.)
10. SWS: Bi-variate analysis (correlation, covariance etc.)
11. SWS: Outlier detection and treatment
12. SWS: Exploring nonlinear relations, curve fitting and smoothing
13. SWS: Trends, frequencies and prediction
14. SWS: Machine Learning: Custering, classification

Programmierung mit Python, Tabellenkalkulation mit Excel