Data Literacy Articles

In the second entry of data demystified, we’ll take a look at the two most common data types: Quantitative vs Qualitative Data. For more data demystified blogs, check out the first entry in the series.

In part 11 of data demystified, we examine how AI is covered in the news, how it can lead to the AI hype trap, and how to not fall into it.

In the third entry of data demystified, we’ll define the most common pieces of jargon you hear in data science today. From machine learning to deep learning, here’s all the subfields of data you need to know.

In the fourth entry of data demystified, we’ll break down the four types of analytics. From a descriptive to prescriptive, we’ll look at how analytics can answer the most pressing questions.

In the fifth entry of data demystified, we provide an overview of the basics of descriptive statistics, one of the fundamental areas of data science.

In the final part of data demystified, we outline the most common types of AI bias, and why data literacy helps avoid harmful impacts from AI.

Learn the critical difference between correlation and causation in data analysis. Understand real-world examples and avoid common pitfalls in interpreting data.

We’re excited to announce our four-part webinar series covering organizational data maturity. Sign up and gain a full overview of what is required to become a data-driven organization.

Discover the most in-demand data skills and why employers are seeking skilled individuals. Plus. find out how you can improve your data knowledge in key areas.

Improve your students’ data literacy by working on collaborative data science projects in DataLab, DataCamp's AI-enabled data notebook.

Discover why data literacy matters and learn how it can help your team thrive in an increasingly data-driven world.

Learn about the origins and development of this revolutionary business intelligence tool. Discover why industries across the globe rely on Power BI to visualize their data and inform their decision-making.