Data Visualization: Principles and Practices

Related lectures (52)

Explores techniques and applications of data visualization, emphasizing the importance of effective communication and unconventional examples.

Covers the basics of creating and customizing charts in Excel.

Covers principles and techniques of data visualization, emphasizing storytelling, anomaly detection, and effective communication of results.

Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.

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

Explores EPFL's doctoral theses history, data visualization, and the innovative D3 approach.

Covers the analysis of KMnO4 concentrations using data from a CSV file.

Covers the practical use of QGIS for spatial data analysis and visualization, including georeferencing historical maps and manipulating vector data.

Explores data analysis techniques, emphasizing time series prediction in a flipped classroom setting.

Covers best practices and guidelines for big data, including data lakes, typical architecture, challenges, and technologies used to address them.

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

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

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.

Explores data visualization techniques, design impact, and interactive applications for effective information communication.

Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Explores systemic environmental assessment, national material flow analysis, and urban metabolism dashboard development for Zurich using open data.

Explores data wrangling, numerical data handling, and scientific visualization using Python for engineers.

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Page 1 of 3