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

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.

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

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

The current market dynamics, state of inflation and fears of a recession have made recruiting for technical talent even more complex. Despite this, there are opportunities for technical recruiters to stay competitive and beat out the competition for top top talent.

Learn the differences and similarities between Power BI and Excel, and when you should use each.

Machine learning is one of the hottest topics in data science. Here is a list of 15 books to break into the field or become an expert

Machine learning projects for beginners, final year students, and professionals. The list consists of guided projects, tutorials, and example source code.

In this article, we will cover the main differences between Python 2 and 3, which one is the best, and which one you should go for to start your data science journey

Discover what supervised machine learning is, how it compares to unsupervised machine learning and how some essential supervised machine learning algorithms work