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- Basics operations in pandas
- Loading data
- Slicing and filtering data
- Identifying and replacing missing data
- Converting and exporting pandas DataFrames
- Creating plots with Matplotlib
- Using Matplotlib wrappers like Seaborn
- Creating heatmaps, histograms, and subplots
Skill Level Intermediate
- The amount of data being generated is enormous. Today, most companies are looking to use their data to create efficiencies, go after new markets, and build new products. But, it's one thing to capture data, if you want to communicate and understand your data, you need to use tools to help you create data visualizations. A great way to do this is using Python. Python has a powerful data science ecosystem with libraries that can help you create visualizations that are compelling and ready for publication. My name is Michael Galarnyk, I'm a data scientist, a Python instructor, and blogger.
In this course, I want to show you how to build compelling data visualizations using Python. I'll give you an overview of the tools available, then I'll share how to manipulate your data using Pandas, and how to take that data and create visualizations using Matplotlib. I'll also show you how to create boxplots, heat maps, histograms, and more. By the end of this course, you'll feel confident and ready to go build your own powerful visualizations using Python. So, if you're ready to dive in, let's go.
Data Visualization for Data Analystswith Bill Shander1h 31m Beginner
Data Visualization in R with ggplot2with Mike Chapple2h 27m Intermediate
1. Data Visualization Tools
4. Advanced Plotting
Next steps1m 10s
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