Data Visualization with Matplotlib & Seaborn

This is a hands-on, project-based course designed to help you learn two of the most popular Python packages for data visualization: Matplotlib and Seaborn.

We'll start with a quick introduction to data visualization frameworks and best practices, and review essential visuals, common errors, and tips for effective communication and storytelling.

From there we'll dive into Matplotlib fundamentals, and practice building and customizing line charts, bar charts, pies & donuts, scatterplots, histograms and more. We'll break down the components of a Matplotlib figure and introduce common chart formatting techniques, then explore advanced customization options like subplots, GridSpec, style sheets and parameters.

Finally we'll introduce Python's Seaborn library. We'll start by building some basic charts, then dive into more advanced visuals like box & violin plots, PairPlots, heat maps, FacetGrids, and more.

Throughout the course you'll play the role of a Consultant at Maven Consulting Group, a firm that provides strategic advice to companies around the world. You'll practice applying your skills to a range of real-world projects and case studies, from hotel customer demographics to diamond ratings, coffee prices and automotive sales.

If you're a data scientist, BI analyst or data engineer looking to add Matplotlib & Seaborn to your Python skill set, this is the course for you.

COURSE CONTENTS:

• 7.5 hours on-demand video (12.5 CPE credits)

• 15 homework assignments (plus 3 course projects)

• 4 quizzes

• 2 skills assessments (1 benchmark, 1 final)

COURSE CURRICULUM:

WHO SHOULD TAKE THIS COURSE?

• Analysts or BI professionals looking to learn Matplotlib & Seaborn
• Aspiring data scientists who want to build Python data visualization skills
• Anyone interested in learning one of the most popular open source programming languages in the world

WHAT ARE THE COURSE REQUIREMENTS?

• Jupyter Notebooks (free download, we'll walk through the install)
• No advance preparation is required (basic familiarity with programming is a plus, but not a prerequisite)

WHAT ARE THE COURSE OBJECTIVES?

• Identify key data visualization best practices, including essential visuals and their use cases, common errors, and tips for formatting & effective storytelling

• Identify and interpret Python syntax for creating chart objects with the Matplotlib library, including the PyPlot API and Object Oriented interfaces

• Identify the components of a Matplotlib chart object, including titles, legends, colors, styles, annotations, axis ticks, subplots, and GridSpecs

• Identify and interpret the Matplotlib plotting functions for different chart types, including line charts, bar charts, pie charts, histograms, and scatterplots

• Identify and interpret the Seaborn plotting functions for different chart types, including bar charts, histograms, boxplots, violin plots, heatmaps, and linear relationship plots

CPE ACCREDITATION DETAILS:

• CPE Credits: 12.5

• Field of Study: Information Technology

• Delivery Method: QAS Self Study

Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.

For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.

*Last Updated: October 1, 2022