Data Visualization using MatPlotLib & Seaborn

Learn to visualize data with Python libraries such as matplotlib and seaborn, presenting analyses and predictive models as graphical representations to support business decisions.

Learn Matplotlib basics for data visualization and Seaborn context, covering graph functions, data inputs, 3d visuals, and limitations.

Explore data visualization with Matplotlib and Seaborn, learn to import libraries, create line, bar, pie charts and histograms, customize axes, labels, and figure sizes.

Harness pyplot to create bar, pie, histogram, scatter, and 3d plots that turn data into clear graphical representations for analysis and reporting.

Learn to create and customize bar, pie, histogram, scatter, and 3d plots with pyplot in Matplotlib, including stacked bars, explode slices, labels, legends, and 3d projections for comparing multi-series data.

Discover how to generate multiple plots in a graph to reveal insights and improve a business using Matplotlib and Seaborn.

Learn to create multiple plots in one graph using subplot in MatPlotLib, specifying rows, columns, and positions to arrange and customize each chart with titles and labels.

Explore Seaborn plotting functions and how to generate graphs from data using the main function.

Explore seaborn plotting functions and matplotlib basics to visualize statistical relationships, load built-in datasets, and customize high-level visualizations using seaborn’s interface.

Explore plotting with categorical data to spot analysis trends and reveal insights, using matplotlib and seaborn to visualize complex data.

Explore Seaborn plotting functions to visualize statistical relationships for single and multiple variables. See how categorical data is plotted, and how the kind parameter shapes scatter and line plots.

Learn how to build multi-plot grids in Matplotlib and Seaborn, explore scatter plot analyses, and understand how combining blocks can lead to incorrect conclusions.

Explore data visualization with seaborn and matplotlib by building relational plots, loading datasets, and creating scatter and line charts to compare price, mileage, model, and transmission.

Explore plot aesthetics and the link to analysis in data visualization, choosing effective visuals and avoiding web-based tools for practical presentation.

Explore plot aesthetics in Matplotlib and seaborn, crafting scatter plots, line plots, and box plots with color schemes to reveal numerical and categorical relationships clearly.

Explore what data science is, from statistics and machine learning to data analysis and visualization, and learn the data lifecycle from collection to reporting.

Understand what machine learning is, how it differs from traditional programming, and the role of statistics in data science, with supervised and reinforcement methods and linear regression basics.

Explore how validation and multilingual contexts shape data presentations and audience understanding. Learn to craft clear visuals and concise explanations that align with presented relationships and basic analytical approaches.

Explore data visualization using a used cars dataset to build and interpret box plots, scatter plots, and distribution insights, including mean and median comparisons and left-right skew.

Explores open source data visualization with macro and medium directions, discusses graphical representations, categorical data, and the differences between sequel concepts, alongside relevant functions and practical applications.