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

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 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 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 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.

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

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

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