What are key analytics techniques?

There are several key analytics techniques that are commonly used in data analysis and business intelligence:

Descriptive Analytics: This type of analytics involves summarizing and describing data, such as calculating averages and frequencies. It provides a snapshot of the past and helps to understand the current state of the business.

Diagnostic Analytics:
This type of analytics is used to identify the cause of a particular problem or issue. It involves drilling down into the data to understand the underlying causes of a particular phenomenon.

Predictive Analytics:
This type of analytics involves using statistical techniques and models to make predictions about future events or trends. It can be used for a wide range of applications, such as customer behavior analysis, fraud detection, and forecasting.

Prescriptive Analytics:
This type of analytics involves using data, models and algorithms to recommend actions to take in a specific situation. It can be used to optimize processes, improve operations and to make strategic decisions.

Supplemental Analytics techniques 

Machine Learning:
Machine learning is a subset of advanced analytics that involves using algorithms to analyze data and identify patterns and make predictions. It can be used for a wide range of applications, such as image recognition, natural language processing, and predictive modeling.

Data Mining:
Data mining is the process of discovering interesting patterns, relationships, and insights from large sets of data, it involves the use of algorithms, statistical models and machine learning techniques to extract insights from data.

Data Visualization:
Data visualization is the process of representing data in graphical format, using charts, graphs, and other visual elements to help understand and communicate insights from data.

In summary, descriptive, diagnostic, predictive and prescriptive are key analytics techniques that are commonly used in data analysis and business intelligence. They help organizations to gain insights, make predictions, and optimize operations. The choice of the appropriate technique will depend on the specific use case and the data available.