This article covers training models in machine learning and why it is an essential process that equips machines with the ability to spot patterns, predict outcomes, identify anomalies, and test correlations.

This article defines behavioral analytics, explains how to do it, and why it is important. It also explains what behavioral data consists of and introduces a variety of behavioral analytics tools.

This article defines diagnostic analytics, explains how to use diagnostic analytics in your business, and gives examples of the kinds of questions diagnostic analytics aims to answer.

Data analytics sets up the context and process for analyzing data. Statistics verifies it with rigorous proof. These processes must be well understood in order to efficiently sort, communicate, and validate data-based business decisions.

This article compares data analytics and business analytics, examining the data sources, analytical approaches, and deliverables for each. It also covers how business analytics fits into the data analytics lifecycle.

This article gives an overview of data analytics and explains the different types of data analytics, why data analytics is important, and how it’s different from business intelligence.

This article explores the differences between data analytics and data visualization, and explains the primary data visualization strategies you should use and how they benefit your business.

In this article, we explain the differences between data analytics and data science, and discuss the job roles, skill sets, and responsibilities of both a data analyst and a data scientist.

The data your business collects for analysis and research will fall into one of two categories — quantitative or qualitative. This article explains what quantitative and qualitative data are and how each can be used in your business.

For a successful data analytics project, it’s important to have streamlined processes in place. In this article, we detail the different steps of the data analytics process and give valuable tips to guarantee the success of your data-driven project.

Understanding the role that data analysis has to play in the data analytics lifecycle is vital to developing a healthy data system that produces value from your incoming data.

This article describes the data analytics lifecycle — question discovery, data preparation, model planning, building and executing the model, communicating the results, and operationalizing the process — and why each phase is important.

This article explains the differences between data analytics and business intelligence in terms of scope, temporal focus, and frequency. It discusses the benefits of data analytics and business intelligence and covers some useful BI techniques.

This article defines descriptive analytics, explains how it works and its benefits for your business, and it gives real-world examples of descriptive analytics, such as financial metrics, social media engagement, and web traffic reports.