2025 Data Visualization in Tableau & Python (2 Courses in 1)

Explore Tableau Desktop’s interface, learn to connect to files or servers, load workbooks and dashboards, and retrieve data from Excel, JSON, PDF, or cloud servers like Salesforce and MySQL.

Load and clean a text data source in Tableau using the data interpreter, hide and rename fields, and reflect changes in the worksheet for HR analytics data.

Load a Microsoft Excel file into Tableau, select the target sheet, and explore measures and dimensions to build a graph by dragging fields to columns and rows.

Load the Olympic athletes Excel dataset in Tableau, identify dimensions and measures, and create countrywise gold medal visualizations using bars, colors, and various chart types.

Learn to format the title in Tableau by applying bold styling, centering, choosing fonts, and adjusting font size. Apply colors, shading, and borders to enhance the heading.

Join two tables in Tableau and create a category-wise bar chart from a downloadable Excel data set, using category for color and sales for labels.

Explore creating horizontal, stacked, and side-by-side bar charts using the show me panel, with one or more dimensions and measures, and at least three fields for side-by-side charts.

Plot a line chart in Tableau, explore discrete and continuous lines, and use shipment date with profit measures to reveal trends from 2011 to 2015.

Create area charts in Tableau by selecting date, dimensions, and measures to plot continuous or discrete areas; compare quantity, sales, and profit across years on multiple charts.

Learn to create circle views and side-by-side circles in Tableau by selecting dimensions and measures, visualizing sales by subcategory and region, and previewing profit details on hover.

Learn to generate scatter plots using zero or more dimensions and two to four measures, exploring region, country, and city level sales and quantity with hover details.

Learn to generate and customize pie charts by selecting dimensions and measures to compare sales and profit across region, country, state, city, and category breakdowns in Tableau.

Create a histogram in Tableau by loading Excel data, joining orders with order breakdown, and visualizing a single measure such as sales or profits to show the distribution.

Create text tables and highlight tables by arranging one or more dimensions as columns and one or more measures as rows, using drag-and-drop to visualize data.

Create symbol maps and other maps in Tableau using a geographical dimension and measures, color by country, and hover to display country, sales, discount, profit, and quantity details.

Create heat maps in Tableau using two dimensions and two measures, visualizing category and subcategory with sales and profit, with hover details and color by segment.

Learn to build a four-sheet Tableau dashboard for Olympic athletes data, with bronze medals bar chart, gold medals packed bubble chart, silver medals map, and total medals treemap.

Explore Matplotlib for visualizing data by plotting line, bar, scatter, pie, area plots, and histogram plots with Pyplot, to reveal clear data insights.

Plot a 2d line graph using matplotlib in python by initializing x and y data, plotting with plt.plot, labeling axes, and adding a title to show performance across five semesters.

Convert line graphs to bar charts in Python using matplotlib by replacing plt.plot with plt.bar, and see immediate changes in the bars as data updates, with titles and labels.

Convert a bar chart to a scatter graph using the same data, add titles and labels, and view the scatter chart with data points.

Create a histogram graph in Python using Matplotlib, with the x axis data and monotonically increasing bins. Use the legend function to label and display the histogram.

Create and customize a pie chart with Matplotlib by adjusting the explode parameter to separate wedges, and display the plot with a title using the pie function.

Import matplotlib.pyplot as plt and create a figure object to prepare a 3D projection for plotting. Run the code to display a 3D projection, which currently shows no graph yet.

Create a 3D line graph by generating a 3D plot with lines and projecting the x, y, z axes to reveal the final visualization.

Explore Seaborn, a Python visualization library built on Matplotlib, and learn to import NumPy, Pandas, and Seaborn. Load tips.csv with Seaborn and Pandas; inspect df.head and df.tail, 244 rows.

Plot visualizations with Seaborn, including swarm plots, violin plots, facet grids, and heatmaps, using the tips dataset (total bill, tip, sex, smoker, day, time, size) and inspect data with head.

Explore how to create a swarm plot in seaborn to compare tips by day and gender, with tips.csv, using white or dark grid styles and gender-based color palettes.

Create violin plots to reveal density in numerical data, comparing male and female distributions of total bill with seaborn, using a gold color scheme for clear visual emphasis.

Explore seaborn facet grids to plot time and sex across four combinations, switching between histograms and scatter plots with color cues for total bill and tip by lunch or dinner.

Visualize a five by three matrix of uniform data in the 0 to 1 range with seaborn heatmaps, mapping each value to color for a clear graphical representation.

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