Data Visualization: Principles and Practices

Related lectures (32)

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

Explores different types of graphs achievable with Matplotlib, showcasing a variety of visualization techniques.

Covers a wide range of topics in Matplotlib, including simple plots, subplots, and various types of charts.

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

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

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

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

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.