Data Analytics Methods and Techniques

- Overview

Data Analytics Methods and Techniques are the tools and structured approaches used to examine raw data, transform it into meaningful information, and extract actionable insights to support decision-making. 

The primary methods are descriptive (summarizing past data), diagnostic (finding causes of past outcomes), predictive (forecasting future outcomes), and prescriptive (recommending actions) analyses. 

Techniques include statistical analysis, data mining, regression, factor analysis, and time series analysis, often aided by software like Python, R, and Power BI to identify trends, patterns, and correlations within datasets.

- Types of Data Analytics Methods 

These four types build on each other to provide progressively deeper insights: 

• Descriptive Analysis: Focuses on "what happened?" by summarizing past data to identify trends and key performance indicators.
• Diagnostic Analysis: Explores "why it happened?" by examining data to determine the root causes of past events or outcomes.
• Predictive Analysis: Answers "what will happen?" by using statistical models and historical data to forecast future trends and outcomes.
• Prescriptive Analysis: Addresses "what should be done?" by recommending specific actions to achieve desired results based on data insights.

- Common Data Analytics Techniques

• Statistical Analysis: A broad category that includes methods for analyzing data, finding patterns, and testing hypotheses.
• Data Mining: The process of discovering patterns and knowledge from large datasets using techniques like association rules and classification.
• Regression Analysis: A technique to model the relationship between variables and predict an outcome based on one or more independent variables.
• Factor Analysis: Used to identify relationships between variables and uncover underlying factors that explain those relationships.
• Time Series Analysis: Analyzing data points collected over time to understand trends and make forecasts.
• Data Visualization: Creating charts, graphs, and dashboards to present complex data in an easy-to-understand visual format.

- Tools and Process

Data analysts use a variety of tools, including spreadsheets, data visualization tools (like Tableau and Power BI), and programming languages (such as Python and R). 

The overall process involves collecting, cleaning, transforming, and modeling raw data to extract valuable, actionable insights that inform business strategies and decision-making.