Many nonprofits need volunteers to participate in their workshops and training. Therefore, all year long we try to find volunteering opportunities.

A list of the must-have skills every data scientist should have in their toolbox, including resources to develop your skills.

In part 10 of data demystified, we’ll dive deep into the world of data visualization, continuing with visualizations that capture distributions.

In part eight of data demystified, we’ll dive deep into the world of data visualization, starting off with visualizations that capture trends.

In part nine of data demystified, we’ll dive deep into the world of data visualization, continuing with visualizations that capture relationships.

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.

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.

This September, we’re dedicating an entire month to covering data literacy. Here’s what you can expect.

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 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 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 part six of data demystified, we’ll break down one of the biggest fallacies in data: Correlation does not imply causation