Scientific Computing

7.

Data Analysis and Visualization for Scientific Computing

7.1.

Data Management

7.1.1.

Data Types and Structures

7.1.2.

Handling Large Datasets

7.1.3.

Standard Data Formats

7.1.3.1.

7.1.3.2.

Data Organization

7.1.3.3.

7.1.3.4.

Parallel Access

7.1.3.5.

7.1.3.6.

Metadata Handling

7.1.3.7.

Use in Climate Science

7.1.3.8.

CSV and Text Formats

7.1.3.9.

7.1.4.

Database Systems

7.1.5.

I/O Strategies and Performance

7.1.5.1.

7.1.5.2.

7.1.5.3.

7.2.

Data Analysis Techniques

7.2.1.

Exploratory Data Analysis

7.2.2.

Statistical Analysis of Simulation Data

7.2.2.1.

Descriptive Statistics

7.2.2.2.

Hypothesis Testing

7.2.2.3.

Confidence Intervals

7.2.3.

Time-Series Analysis

7.2.3.1.

Autocorrelation

7.2.3.2.

Spectral Analysis

7.2.3.3.

7.2.3.4.

Seasonal Decomposition

7.2.4.

Dimensionality Reduction

7.2.4.1.

Principal Component Analysis

7.2.4.2.

Singular Value Decomposition

7.2.4.3.

Independent Component Analysis

7.2.4.4.

7.2.5.

Clustering Analysis

7.2.6.

Classification Methods

7.2.7.

Error Analysis and Uncertainty Quantification

7.2.7.1.

Propagation of Uncertainty

7.2.7.2.

Monte Carlo Error Analysis

7.2.7.3.

Bootstrap Methods

7.3.

Scientific Visualization

7.3.1.

Principles of Effective Visualization

7.3.1.1.

Clarity and Readability

7.3.1.2.

Color Mapping and Perception

7.3.1.3.

Visual Perception

7.3.1.4.

Design Principles

7.3.2.

7.3.2.1.

7.3.2.2.

7.3.2.3.

7.3.2.4.

7.3.2.5.

7.3.2.6.

7.3.2.7.

7.3.3.

3D Visualization

7.3.3.1.

7.3.3.2.

7.3.3.3.

Slicing Techniques

7.3.3.4.

Volume Rendering

7.3.3.5.

Vector Field Visualization

7.3.3.6.

7.3.3.7.

7.3.3.8.

Particle Tracing

7.3.4.

Animation and Interactive Visualization

7.4.

Visualization Tools and Libraries

7.4.1.

Python Libraries

7.4.1.1.

7.4.1.2.

7.4.1.3.

7.4.1.4.

7.4.1.5.

7.4.2.

R Visualization

7.4.3.

Standalone Applications

7.4.3.1.

7.4.3.2.

7.4.3.3.

7.4.4.

Web-Based Visualization

7.5.

Reproducibility in Computational Science

7.5.1.

Reproducible Research Principles

7.5.2.

Workflow Management

7.5.2.1.

Automation Tools

7.5.2.2.

Pipeline Design

7.5.2.3.

Make and Makefiles

7.5.2.4.

7.5.3.

Data Provenance

7.5.3.1.

Tracking Data Lineage

7.5.3.2.

Metadata Standards

7.5.3.3.

Version Control for Data

7.5.4.

Literate Programming

7.5.4.1.

Jupyter Notebooks

7.5.4.2.

Integration of Code and Documentation

7.5.4.3.

7.5.5.

Containerization

7.5.6.

Virtual Environments

Previous

6. High-Performance Computing

Go to top

Next

8. Advanced Topics and Applications