A branch of advanced analytics that uses historical data to predict future outcomes, helping in decision-making.

A subset of artificial intelligence where algorithms learn from data to make predictions or decisions without explicit programming.

A versatile programming language widely used in data science for its simplicity and extensive libraries.

The graphical representation of information and data to communicate insights clearly and effectively.

The process of selecting and transforming variables to improve model performance in machine learning.

An approach to analyzing data sets to summarize their main characteristics, often using visual methods.

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

Parameters whose values are set before the learning process begins, affecting the model's performance.

A popular Python library for machine learning that provides simple and efficient tools for data analysis.

The process of correcting or removing inaccurate records from a dataset to ensure data quality.

The identification of data points that differ significantly from the majority of the data, which may indicate errors or variability.

A subset of data used to assess the performance of a model during training, ensuring it generalizes well.

The process of adjusting model parameters to improve its performance on a given dataset.

A method used to evaluate the strength and direction of relationships between two variables.

A collection of data, typically organized in a structured format, used for analysis and modeling.

A Python library that provides data structures and tools for data manipulation and analysis.

A fundamental package for scientific computing in Python, providing support for large, multi-dimensional arrays.

Quantitative measures used to assess the performance of a predictive model.

Software or libraries used to create graphical representations of data, enhancing interpretability.

Conclusions drawn from data analysis that can directly inform decision-making and strategy.

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

Processes that involve randomness and unpredictability, often used in predictive modeling.

The identification of rare items, events, or observations that raise suspicions by differing significantly from the majority.

A statistical technique that deals with time-ordered data points to identify trends and patterns.

The practice of basing decisions on data analysis rather than intuition or observation.