The process of discovering patterns and knowledge from large amounts of data, often used in customer analytics.

A technique that groups similar data points together, helping to identify customer segments.

A method for discovering interesting relationships between variables in large datasets, often used in market basket analysis.

A popular open-source software for data mining, providing tools for data preprocessing, classification, and clustering.

The process of cleaning and transforming raw data into a usable format for analysis.

The measure of data's accuracy and completeness, crucial for reliable analysis and insights.

The principles guiding the responsible use of data, ensuring privacy and compliance with regulations.

A model used for classification and regression tasks, visualizing decisions and their possible consequences.

Computational models inspired by the human brain, used for complex pattern recognition and predictive analytics.

The graphical representation of data, making complex information more accessible and understandable.

The practice of using data to tell a compelling story, helping to convey insights effectively.

Techniques that use statistical algorithms to identify the likelihood of future outcomes based on historical data.

The process of dividing a customer base into groups for targeted marketing strategies.

A technique that analyzes co-occurrence of items in transactions, useful for understanding purchasing behavior.

A data science platform that provides tools for data preparation, machine learning, and predictive analytics.

The ability to identify patterns and regularities in data, fundamental to data mining.

A hands-on initiative where learners apply techniques to analyze a dataset and extract insights.

Real-world examples that illustrate successful applications of data mining techniques.

The ability to interpret data and draw meaningful conclusions, essential for data-driven decision-making.

Collections of related data points, typically used for analysis and modeling.

A reflective process where learners evaluate their understanding and performance throughout the course.

Assessment tools that outline criteria for evaluating students' work and understanding of concepts.

The culminating assignment where students showcase their data mining skills and insights from a comprehensive analysis.

A technique for assessing how the results of a statistical analysis will generalize to an independent dataset.

A statistical method to determine if there is enough evidence to reject a null hypothesis.