The Complete Data Visualization with Python [2022]

Begin the course section by exploring the basics of data visualization, including bar graphs, grouped bar graphs, and scatterplots. Complete two projects to practice these concepts on real-world datasets.

Learn to plot a line chart showing stock prices over time by converting a pandas DataFrame to a time series, setting the date as the index, and plotting.

Create a bar plot in Python using Pandas and NumPy, with a subplot layout. Annotate bars with data values and customize the x-axis labels with rotation.

Build a bar chart of yearly passengers from a data frame, using pandas to group by year and seaborn to color-code by year, with labeled axes and a title.

Create a grouped bar chart in seaborn using the Titanic dataset. Load the data, set class and sex as the categorical axes, and visualize the aggregated counts.

Explore how a pie plot visualizes category contributions by setting data, labels, and sizes, and by placing a slice slightly outside the circle with a start angle for percentage insights.

Create two pie charts on a single graph using Titanic survival data by class and gender, with labeled slices and color mapping.

Learn how to create a scatter plot in Python to compare two variables and assess correlation, using monthly sales data, labeled x and y axes, and a clear figure layout.

Import pandas and numpy, load a dataset into a dataframe, and visualize a numerical attribute with a histogram and a box plot to show distribution.

Analyzes categorical attributes like marital status, relationship, sex, native country, and income, and explains why pie charts are not ideal, recommending bar charts for clearer differences.

Implement population comparisons with box plots to contrast income groups below or equal to 50k and above 50k, preparing data and plotting in Python.

Use a bar plot to compare population by income categories, such as 50k and under 50k, and refine the code for quicker, clearer visualizations.

Visualize a university dataset with scatterplots and a matrix scatterplot in Python using seaborn and pandas, exploring relationships between tuition, graduation rate, enrollment, and accepted applications.

Develop an auto visualization with a color-coded contingency table to examine the relationship between sex and income, then visualize it with a Seaborn heat map.

Continue project 2 by creating a color coded contingency table and a probability table from column sums, then visualize the relationship between variables in the photo dataset.

Continue the data visualization project by transforming a categorical variable, building a contingency table of age groups and category, and visualizing the relationship with a heat map in pandas.

Visualize the relationship between education and age using visualization techniques, transform education into a numeric form, and conclude there is no relationship between age and education.

Explore creating and refining a scatter plot with Python data viz tools, linking life expectancy, well-being, and the Happy Planet Index by region using pandas, seaborn, and Altair.

Enhance an advanced scatter plot with python by adding zoom, hover tooltips, and displays of life expectancy, happiness, well-being, and inequality of outcomes for interactive data insights.

Add select and highlight functionality to the scatter plot, turning selected areas red while unselected ones stay light gray, enabling interactive selection and deselection.

Explore advanced scatter plots in python, master zooming and region selection, and copy and paste code to render interactive region colors for data visualization.

Explore advanced scatter plots by building multiple graphs, adding features, and arranging axes horizontally and vertically with color and region cues.

Master advanced scatter plot techniques by implementing interactive value-based selections and region-driven color coding. Analyze region patterns as you prepare to move from advanced scatter plots to bar plots.

In this advanced bar chart part 1 video, we load data, build a mock bar chart, adjust color and weight, and add a line for a clearer visualization.

Explore advanced bar chart techniques in Python data visualization, enabling click-and-drag selection, interactive cursor movement, and dynamic bar highlighting, with a preview of a heat map.

Create and customize heat maps in Python, adjust color scales and legends, add circle markers, troubleshoot layout, and interpret solid visual results.

Practice blending a bar plot with a heat map, using region selection, color encoding, and circle-based visuals to improve data visualization in Python.

Practice bar plots, heatmaps, and circles on the Google Play Store dataset using Python, exploring apps by content rating category and cleaning data with pandas.

Learn the basics of Plotly by creating an empty figure with go.Figure, assigning it to a variable, and iteratively adding or modifying elements to see changes.

Explore basic plotting with Plotly by creating a figure from a random dataset, labeling x and y axes, and adding descriptive titles; understand when legends improve data clarity.

Learn how to load and filter population data with pandas, build a subset excluding regions, identify the top 20 countries by population in 2010, and visualize the results.

Continue implementing a data visualization app that plots country population by year, formats the y-axis in billions, adds a figure title, enables year selection, zoom, and pan, and PNG export.

Explore building a Dash web app in Python, isolating the environment, and creating a dropdown year selector with a graph and callback to display population data.

Explore adding dimensions in Python data visualizations with color and animation to tell stories from the happiness report (2002-2019), focusing on healthy life expectancy and GDP per capita.

Build an interactive Python data visualization by defining functions, plotting with labels, and using a year-range slider to dynamically update the graph across selected years.

Explore four-dimensional visualizations in Python by encoding a continent as the fourth dimension with color and time. Compare continent trends as data evolves.

Apply data visualization concepts by coding the fifth dimension with population data, adjusting scales and confidence intervals, and preparing the data frame for clear plotting in the project.

Learn to build a Python bubble plot comparing countries, using population and GDP per capita on the axes with bubble size encoding a third metric, plus color and axis labeling.

Continue with advanced plotting methods in Python using Seaborn and pandas to build a dataframe, define X and y, and plot a regression line with false parameter for practice.

Apply advanced plotting methods from part 3 of the Python data visualization course to a real dataset, creating and troubleshooting visualizations while analyzing employee satisfaction and turnover.

Master advanced plotting with Python by applying kernel density estimation, non-parametric and parametric concepts, and creating violin plots, joy plots, and bivariate distribution visuals.

Celebrate your progress in deep learning and pursue becoming a successful deep learning engineer by downloading advice from Cargill and UCI Rice Bowl to build models from their dataset.