Inside activities: Taxonomies and Cognitive Load

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Delves into data physicalization, expressiveness, feminist visualization, and the balance between exploration and explanation in data visualization.

Unsupervised Learning: Principal Component Analysis

Covers unsupervised learning with a focus on Principal Component Analysis and the Singular Value Decomposition.

Principal Component Analysis: Dimension Reduction

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.

Visual Extensions in the Workplace

Explores the use of visual extensions like AR and VR in the workplace, emphasizing their benefits, challenges, and importance for enhancing workplace efficiency.

Data Visualization: Techniques, Tools & Concepts

Covers data visualization techniques, tools, and concepts for effective data representation.

Air Pollution Data Analysis

Covers the analysis of air pollution data, focusing on R basics, visualizing time series, and creating summaries of pollutant concentrations.

Data Visualization: Techniques and Examples

Explores data visualization techniques using sports, historical events, and literature examples.

Unsupervised learning: Young-Eckart-Mirsky theorem and intro to PCA

Introduces the Young-Eckart-Mirsky theorem and PCA for unsupervised learning and data visualization.

Free Piston Simulation

Demonstrates the simulation of a free piston system using COMSOL Multiphysics software.

Data Science Essentials: Pandas, Numpy, Matplotlib

Introduces Pandas, Numpy, and Matplotlib for data analysis and visualization in Python.