This course can also be delivered using R.
Although most people are familiar with data visualization in the context of business intelligence dashboards, this is only the tip of the iceberg. In this hands-on course, you will learn to use visualizations the way data scientists use them—to get to the “why” of what’s happening in the business.
In this course, you will learn to use visualizations like histograms, scatter plots, and box plots to perform exploratory data analysis (EDA). EDA is not only a fundamental skill of data analysts/scientists, it’s also a fundamental skill of any data-literate professional.
You will learn to use data visualizations to find patterns in data that provide insights into business questions like:
- Does paid conversion vary by age?
- How does component mix influence product lifespan?
- What are the trends in our sales over the past X years?
- Given the data, could we build a useful machine learning model?
Via a series of hands-on labs, you will craft powerful data visualizations using the plotnine library in Python.
The goal of this course is for you to return to work and start employing these techniques to analyze your data.
Want to know the best part?
Although the course uses Python because it is free, all the concepts/techniques you will learn apply to any tool you might use—even Microsoft Excel!
Your Team Will Learn
- How to frame your analysis in terms of business questions
- How visualizations can be used to analyze data and get to the “why”
- Visual analysis techniques for numeric and categorical data
- Visual analysis techniques for data over time
- How to create data visualizations in Python using plotnine
- How to increase the power of your visual data analyses by adding dimensions
- Additional resources to extend your learning
- Business and data analysts
- BI and analytics developers and managers
- Business users
- Data scientists
- Anyone interested in learning to analyze their business data visually
No skills in programming or statistics are required!
Prerequisite: Students must be familiar with Python and Jupyter notebooks or complete the pre-recorded course “Python Quick Start” prior to the class. This pre-recorded course will be made available in advance to any students who need it.
Attendees will need a laptop computer with specific software installed before the session. In advance of the class, attendees will receive detailed software download and installation instructions.