Data Analytics Methods and Techniques

• Predictive analytics: Uses machine learning, statistical models, and data mining to predict future events or trends.
• Statistical analysis: Involves collecting and analyzing data samples.
• Diagnostic analysis: Uses techniques like data discovery, correlations, data mining, and drill-down to understand data connections and identify patterns.
• Descriptive analytics: Uses simple mathematical operations to perform statements about measurements and samples. It's one of the most common ways companies use analytics to stay updated on trends and operational performance.
• Prescriptive analytics: Uses data, statistical analysis, and modeling techniques to identify the best step to take in a given situation. It provides recommendations on actions to achieve the desired outcome.
• Text analytics: Also known as natural language processing (NLP) or text mining, it involves analyzing large volumes of text data to identify insights, trends, and patterns that can inform business decisions.
• Data visualization: Transforms information into a visual context to make it easier for the human brain to understand. 
• Artificial intelligence and machine learning: Use these techniques to analyze data sets and identify patterns. They can help organizations make better decisions by understanding the behavior of employees, customers, and other stakeholders. 

There are two main types of statistical analysis: descriptive and inferential, also known as modeling. 

Descriptive statistics can be useful for two things: