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.

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

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

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.

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