Data Literacy Month at DataCamp is in full swing. Among the live events, podcasts, and articles encouraging individuals and organizations to improve their data literacy, you can learn many key concepts related to data science. One of the focuses is on exploring how different data visualizations work and why they’re important for understanding information.
DataCamp’s three-part series on demystifying data visualizations explores how to capture trends, demonstrate relationships, and explore distributions. As well as exploring how the different types of data visualizations work, they also outline courses that can help you create your own effective visualizations.
Starting with capturing trends, you’ll learn how line charts, spline charts, area charts, and their variations work and how to interpret them. If you’re helping to educate an employee, student, friend, or relative on these concepts, this article is an excellent starting point, full of useful resources.
Next, you’ll look at how data visualizations can demonstrate relationships within data. You’ll get clear information about how different types of bar charts, scatter plots, connected scatterplots, and bubble charts work.
In part three, you’ll look at capturing distributions, exploring histograms, density plots, box plots, and violin plots.
Start Learning Today
Whether you’re an experienced data practitioner responsible for the data literacy development of others or a relative newcomer to data science, you can learn more about data visualization with DataCamp. For a no-code introduction, try Understanding Data Visualization or get started with a tool such as Power BI.
Whatever your goals are, DataCamp can help you develop the data expertise of you or your organization. Get started today!