Big Data refers to the large volume of data that is generated from various sources and is characterized by its volume, variety, and velocity.

Hadoop is a widely used open-source framework that is designed for storing and processing large datasets across clusters of computers.

Data Analytics involves the process of examining large amounts of data to uncover hidden patterns, trends, and insights.

Python is a versatile programming language that is widely used for data analysis and machine learning due to its extensive libraries and ease of use.

Data Preprocessing involves the process of cleaning, transforming, and preparing raw data to make it suitable for analysis.

Clustering is a technique used to group similar data points together based on their characteristics.

Data Visualization involves the process of presenting data in a graphical format to make it more accessible and understandable.

Tableau is a popular tool used for interactive data visualization and exploration, allowing users to create various types of charts and graphs.

Data Mining involves the process of extracting hidden patterns and insights from large datasets using statistical and mathematical techniques.

Regression is a technique used to predict continuous values based on historical data.

Machine Learning involves the process of using algorithms to learn from data and make predictions without being explicitly programmed.

Decision Trees are a type of machine learning algorithm that uses a tree-like structure to make decisions and classify data.

Model Evaluation involves the process of assessing the performance of a machine learning model using various metrics.

Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of data while preserving its important features.

Customer Analytics involves the process of using Big Data analytics to gain insights into customer behavior and preferences.