Data analysis techniques are methods used to inspect, transform, and model data to extract valuable insights. Key techniques include descriptive analysis, which summarizes data using measures like mean and standard deviation, and exploratory data analysis (EDA), which helps identify patterns and anomalies. Inferential analysis makes predictions about a larger population based on a sample, often using hypothesis testing. Predictive analysis employs machine learning algorithms to forecast future outcomes, while causal analysis seeks to establish cause-and-effect relationships. Prescriptive analysis suggests optimal actions based on data, and time series analysis examines data collected over time to uncover trends. Techniques like cluster analysis group similar data points, and dimensionality reduction simplifies complex datasets for easier analysis. Lastly, sentiment analysis analyzes text data to determine sentiment. Each of these methods is crucial for drawing conclusions, making predictions, and supporting informed decision-making.

Here you can learn more about it: https://www.happyloop.com/blog/data-analysis-techniques

Hosted by Ausha. See ausha.co/privacy-policy for more information.