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.

Welcome to Data Demystified! A blog-series breaking down key concepts everyone should know about in data. In the first entry of the series, we’ll answer the most basic question of them all, what exactly is data?

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

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.

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

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

In part seven of data demystified, we’ll break down one of the most common use cases of statistical analysis in data science: A/B testing.

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