Great data visualization is the cornerstone of impactful data science. Visualization helps you to both find insight in your data and share those insights with your audience. Everyone learns how to make a basic scatter plot or bar chart on their journey to becoming a data scientist, but the true potential of data visualization is realized when you take a step back and think about what, why, and how you are visualizing your data. In this course you will learn how to construct compelling and attractive visualizations that help you communicate the results of your analyses efficiently and effectively. We will cover comparing data, the ins and outs of color, showing uncertainty, and how to build the right visualization for your given audience through the investigation of a datasets on air pollution around the US and farmer's markets. We will finish the course by examining open-access farmers market data to build a polished and impactful visual report.
Highlighting your dataFree
How do you show all of your data while making sure that viewers don't miss an important point or points? Here we discuss how to guide your viewer through the data with color-based highlights and text. We also introduce a dataset on common pollutant values across the United States.
Using color in your visualizations
Color is a powerful tool for encoded values in data visualization. However, with this power comes danger. In this chapter, we talk about how to choose an appropriate color palette for your visualization based upon the type of data it is showing.
Uncertainty occurs everywhere in data science, but it's frequently left out of visualizations where it should be included. Here, we review what a confidence interval is and how to visualize them for both single estimates and continuous functions. Additionally, we discuss the bootstrap resampling technique for assessing uncertainty and how to visualize it properly.
Visualization in the data science workflow
Often visualization is taught in isolation, with best practices only discussed in a general way. In reality, you will need to bend the rules for different scenarios. From messy exploratory visualizations to polishing the font sizes of your final product; in this chapter, we dive into how to optimize your visualizations at each step of a data science workflow.
In the following tracksData Visualization
Biostatistician at Vanderbilt
I am currently biostatistician and data scientist at Vanderbilt University. My research focuses on the fusion of machine learning and data visualization to explore and explain electronic health records data. I have worked as a data journalist in the graphics department at the New York Times, a data scientist at Johns Hopkins University Data Science Lab, and as a 'Data Artist in Residence' at data visualization startup Conduce. I am active on Twitter @nicholasstrayer
and blog about data science and visualization with fellow DataCamp instructor Lucy D'Agostino McGowan at Live Free or Dichotomoize