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

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

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

Programmierung mit Python, Tabellenkalkulation mit Excel