Exploratory Data Analysis for Spatio-Temporal Climate Data

From Lsdf
Revision as of 11:50, 9 April 2019 by Benjamin.ertl (talk | contribs) (Created page with "= Description = In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods [https:...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

Description

In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods [link]. EDA for spatio-temporal data has additional challenges to traditional EDA, because of the spatial relations and temporal evolution of observations. The goal of this project is to provide EDA visualizations and summaries for large amounts of climate data (~ millions of observations per dataset).

Task

The main task for this semester is to perform exploratory data analysis on large climatology datasets to discover correlations and patterns that can be used for further analysis.

Tasks

  • Visualizing spatio-temporal data using different dimensionality reduction algorithms
  • Analyzing the main characteristics of large spatio-temporal climate datasets

Requirements

  • Programming experience in statistical computing (Python, R or MATLAB)

Contact

benjamin.ertl@kit.edu