Discover how data visualization can communicate insights far more effectively than text or tables. In this course, you’ll learn how to build data visualizations programmatically using Python, and how you can use visualization to both discover and convey trends in your data.
Overview
Syllabus
- Data Visualization in Data Analysis
- Understand why visualization is important in the practice of data analysis and know what distinguishes exploratory analysis from Explanatory analysis and the role of data visualization in each.
- Design of Visualizations
- Interpret features in terms of level of measurement and know different encodings that can be used to depict data in visualizations.
- Univariate Exploration of Data
- Use bar charts to depict distributions of categorical variables.
- Bivariate Exploration of Data
- Use scatterplots to depict relationships between numeric variables.
- Multivariate Exploration of Data
- Use encodings like size, shape and color to encode values of a third variable in a visualization.
- Explanatory Visualizations
- Understand what it means to tell a compelling story with data and choose the best plot type, encodings and annotations to polish your plots.
- Visualization Case Study
- Apply your knowledge of data visualization to a dataset involving the characteristics of diamonds and their prices.
- Course Project: Communicate Data Findings
- Real-world data rarely comes clean. Using Python, you’ll gather data from a variety of sources, assess its quality and tidiness, then clean it. You’ll document your wrangling efforts in a Jupyter Notebook, plus showcase them through analyses and visualizations using Python and SQL.
Taught by
Josh Bernhard_color and Mike Yi
