Misleading graphs are everywhere. This book will teach you how statistics can be tricky with examples.

Artificial Neural Networks (ANN) has several disadvantages. Yet, we have techniques to overcome them and use it successfully.

Businesses should celebrate modern shifts in the AI/ML technology landscape.

Data warehouse vs. data lake vs. data lakehouse. Why should you choose data lake over data warehouse? And how building a data lakehouse can benefit more?

There are a few things to keep in mind when dealing with data to avoid common data dilemmas and get the most out it.

There are several questions you can ask yourself when deciding which analytics project to take on next. Four criteria have proven to be the most helpful in making this decision.

Large-scale data science teams can have several distinct roles & responsibilities to manage machine learning operations.

Data science has been democratized for the most part. AI is now mainstream! Here's how you can become a citizen data scientist.

85 % of data science projects fail because companies are obsessed with complex models such as deep learning. Simple models often can solve their problems.

Here are the six data quality dimensions and how to use continuous data quality assessments in your business.