Data Visualization: Techniques and Examples

Related lectures (32)

Page 2 of 4

Explores data manipulation, exploration, and visualization in data science projects using Python.

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

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

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

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

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Explores PCA for predicting medals distribution and compressing face images.

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.