Starting out in data science can feel overwhelming, but getting familiar with its terminology can help you build a solid foundation.

Regardless of your professional title, you’ll likely need to know a thing or two about data science at some point in your career.

So whether you’re advancing your skills as a data scientist or navigating new concepts, this comprehensive glossary can beef up your knowledge about everything from “accuracy” to “z-score.”

**📌 Pro-tip: Bookmark this article to refer back to anytime you have a data science related question.**

## A

**Accuracy**

Accuracy is a measure of how often a predictive model is correct.

**Adam optimization**

Adam (adaptive moment estimation) optimization is a popular algorithm used in the training of deep learning models. It is known for its efficiency and low memory requirements.

**Algorithm**

An algorithm is a set of rules or steps designed to solve a problem or perform a specific task. In data science, algorithms analyze data, make predictions, and uncover patterns.

**Alternative hypothesis**

An alternative hypothesis is a claim that contradicts the null hypothesis and suggests an effect or a relationship in the data.

**Anomaly detection**

Anomaly detection is the process of identifying rare items, events, or observations that raise suspicions by differing significantly from the majority of the data.

**ANOVA**

Analysis of variance (ANOVA) is a statistical method used to analyze the differences among group means in a sample.

**Apache Spark**

Apache Spark is a distributed computing system that provides an interface for programming entire clusters with implicit data parallelism.

**API**

An application programming interface (API) is a system that allows different software applications to communicate with each other, enabling integration and data exchange.

**Artificial intelligence**

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction.

**Artificial neural networks**

Artificial neural networks are computational models inspired by the human brain's neural structure and used to recognize patterns and make predictions.

**Auto-regression**

Auto-regression is a time series model where a variable is regressed on its own past values.

## B

**Backpropagation**

Backpropagation is a method for training neural networks by adjusting their weights to reduce errors. It works by moving the error backward through the network to improve accuracy.

**Bagging**

Bootstrap aggregating (bagging) is a machine learning ensemble meta-algorithm that improves model accuracy and stability by combining multiple models.

**Bar chart**

A bar chart is a graphical representation of data where the length of bars represents the frequency or value of data.

**Bayes’ theorem**

Bayes’ theorem is a fundamental theorem in probability theory. It quantifies the probability of an event based on prior knowledge of conditions related to the event.

**Bayesian statistics**

Bayesian statistics is a mathematical approach that applies probability to statistical problems, updating beliefs in light of new evidence.

**Bernoulli trial**

Bernoulli's trial is a random experiment with exactly two possible outcomes, "success" and "failure", in which the probability of success is the same every time the experiment is conducted.

**Bias**

Bias is the systematic error in a model that affects its predictions by consistently skewing results in one direction.

**Bias-variance trade-off**

Bias-variance trade-off is the balance between errors from bias and variance to optimize model performance.

**Big data**

Big data is the term for extremely large datasets that can be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

**BigQuery**

BigQuery is Google's fully managed and serverless data warehouse. It enables very fast SQL queries using the processing power of Google’s infrastructure.

**Binary variable**

A binary variable is a variable that has only two possible values, such as true/false or yes/no.

**Binary classification**

Binary classification is a type of predictive modeling that categorizes data into two distinct classes.

**Binomial distribution**

Binomial distribution is the probability distribution of the number of successes in a fixed number of independent Bernoulli trials.

**Boolean**

Boolean is a data type with two possible values: true or false.

**Boosting**

Boosting is an ensemble learning method that combines a series of multiple weak learners to create a strong learner, primarily to reduce bias and variance in machine learning models.

**Bootstrapping**

Bootstrapping is a statistical technique for sampling data with replacement to estimate uncertainty in a statistic.

**Box plot**

A box plot is a graphical tool to visualize the distribution of a dataset and display its statistical summary such as quartiles.

**Business intelligence**

Business intelligence (BI) involves strategies and technologies used by enterprises for data analysis and management of business information. BI tools and techniques help in transforming raw data into meaningful and useful information.

## C

**Categorical variable**

A categorical variable is a variable that can take on only specific, distinct values representing different groups or categories, which are mutually exclusive and exhaustive.

**Chi-square test**

The chi-square test is a statistical test used to determine the association between categorical variables.

**Classification**

Classification is a supervised learning technique where models learn to assign input data points to distinct categories or classes based on their features.

**Cluster analysis**

Cluster analysis is a technique used to group similar data points into clusters based on their characteristics. This method helps in understanding the structure of data and identifying patterns.

**Computer vision**

Computer vision is the field of study enabling computers to interpret and understand the visual world.

**Concatenate**

Concatenate is the process of joining two or more strings or arrays end-to-end.

**Concordant-discordant ratio**

The concordant-discordant ratio is a measure of agreement in the ranking of paired observations.

**Confidence interval**

A confidence interval is a statistical range indicating where a population parameter is likely to lie based on sample data and a specified level of certainty.

**Confusion matrix**

A confusion matrix is a table used to evaluate the performance of a classification algorithm. It shows the actual versus predicted classifications, helping to visualize errors and accuracy.

**Continuous probability distribution**

Continuous probability distribution describes the probabilities of a continuous random variable’s possible values.

**Continuous random variable**

A continuous random variable is a type of variable that can take on any numerical value within a specified range.

**Convergence**

Convergence is the state where an optimization algorithm stops changing, indicating it has found the best solution.

**Convex function**

A convex function is a mathematical function in which the line segment between any two points on the graph lies above the graph.

**Correlation**

Correlation is a statistical measure that describes the relationship between two variables.

**Cosine similarity**

Cosine similarity is a metric to measure the similarity between two non-zero vectors.

**Cost function**

Cost function is a mathematical function that maps events or values of one or more variables to a real number, representing the cost associated with those values or outcomes in a quantitative manner.

**Covariance**

Covariance is a measure of the relationship between two or more sets of data.

**Cross-validation**

Cross-validation is a model evaluation method used to assess how a predictive model will generalize to an independent dataset. It involves partitioning data into subsets, training the model on some subsets, and testing it on others.

## D

**Dashboard**

A dashboard is a dynamic interface that visually presents key metrics and data trends in real time. It helps users monitor performance and make data-driven decisions by transforming complex data into intuitive visualizations.

**Data analytics**

Data analytics is the process of examining datasets to draw meaningful insights, identify patterns, and make data-driven decisions.

**Data cleaning**

Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, or irrelevant information from datasets to ensure data quality.

**Data engineering**

Data engineering is the practice of designing, developing, and maintaining the infrastructure and systems necessary for storing, processing, and analyzing large volumes of data.

**Data governance**

Data governance is the framework and set of practices that ensure data is properly managed, protected, and available for authorized use within an organization.

**Data lake**

A data lake is a storage repository that holds vast amounts of raw and unprocessed data, allowing for flexible analysis and processing.

**Data mining**

Data mining is the process of discovering patterns, correlations, and anomalies within large data sets to predict outcomes. It combines statistical analysis, machine learning, and database systems.

**Data modeling**

Data modeling is the process of creating a mathematical or logical representation of real-world data to better understand its structure, relationships, and behavior.

**Data pipeline**

A data pipeline is the sequence of steps and processes that move and transform data from its source to its destination, often involving data ingestion, processing, integration, and storage.

**Data preparation**

Data preparation is the process of transforming raw data into a format suitable for analysis or modeling, including tasks like cleaning, formatting, and feature engineering.

**Data science**

Data science is an interdisciplinary field that combines scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

**Data science life cycle**

The data science life cycle is the series of steps involved in a data science project, including problem formulation, data acquisition, data preparation, modeling, evaluation, deployment, and maintenance.

**Data storytelling**

Data storytelling is the art of conveying insights and narratives by using data visualizations, storytelling techniques, and persuasive communication to make data more engaging and understandable.

**Data structure**

A data structure is a way of organizing and storing data to facilitate efficient access, manipulation, and retrieval, such as arrays, lists, and trees.

**Data transformation**

Data transformation is the process of converting or mapping data from one format, structure, or representation to another to meet specific requirements.

**Data type**

A data type is a classification that categorizes the kind of data a variable can hold, such as numerical, categorical, textual, or date.

**Data visualization**

Data visualization is the graphical representation of data through charts, graphs, maps, or other visual elements to facilitate data exploration, analysis, and communication.

**Data warehouse**

A data warehouse is a centralized repository that stores large volumes of structured data from multiple sources. It supports query and analysis, helping organizations make informed decisions.

**Data wrangling**

Data wrangling is the process of cleaning, transforming, and organizing raw data into a format suitable for analysis, modeling, or further processing.

**Database**

A database is a structured collection of data that is organized, stored, and managed in a way that enables efficient retrieval, updating, and querying.

**Dataframe**

A dataframe is a tabular data structure that organizes data in rows and columns, similar to spreadsheets or database tables, commonly used in data manipulation and analysis.

**Dataset**

A dataset is a collection of related data that is organized and stored together, often used for analysis, modeling, or training machine learning models.

**DBScan**

Density-based spatial clustering of applications with noise (DBScan) is a popular clustering algorithm used to group data points based on their density in a given space.

**Decision boundary**

A decision boundary is the dividing line or surface that separates different classes or regions in a classification problem.

**Decision tree**

A decision tree is a supervised learning algorithm that uses a tree-like model to make decisions or predictions by splitting data based on feature conditions.

**Deep learning**

Deep learning is a subset of machine learning involving neural networks with many layers. These networks can model complex patterns in data, enabling advancements in areas like image and speech recognition.

**Decile**

A decile is a statistical measure that divides a dataset into ten equal parts, representing ten percentiles.

**Degree of freedom**

Degree of freedom is the number of independent variables or observations available to estimate or test a statistical hypothesis.

**Dependent variable**

A dependent variable is a number, quantity, or characteristic (variable) that is predicted or influenced by one or more independent variables in a statistical analysis.

**Descriptive statistics**

Descriptive statistics are statistical measures that summarize and describe the main features, patterns, and characteristics of a dataset.

**Dimensionality reduction**

Dimensionality reduction is the process of reducing the number of variables or features in a dataset while preserving as much information as possible.

**Discrete distribution**

Discrete distribution is a probability distribution that describes the probability of occurrence of each discrete or countable random variable, such as Poisson or binomial distributions.

**Discrete random variable**

A discrete random variable takes on distinct, separate values without continuity between them, such as the number of children in a family.

**Dplyr**

Dplyr is a key R package for intuitive and user-friendly manipulation of data frames. Part of the tidyverse, it offers a consistent set of verbs to address common data manipulation challenges.

**Dummy variable**

A dummy variable is a binary variable used to represent categories or levels of a categorical variable in a statistical model.

## E

**Early stopping**

Early stopping is a technique used in training machine learning models to prevent overfitting. It halts the training process when the model's performance on a validation set no longer improves.

**EDA**

Exploratory data analysis (EDA) is the preliminary examination and visualization of data to understand its main features, patterns, and distributions.

**Ensemble learning**

Ensemble learning is a machine learning approach that combines the predictions of multiple models to obtain more robustness and better overall performance.

**ETL**

Extract, Transform, Load (ETL) is the process of extracting data from various sources or systems, transforming it into a consistent format, and loading it into a target system for further analysis.

**Evaluation metrics**

Evaluation metrics are measures used to assess and quantify the performance, reliability, and quality of a predictive model, such as accuracy, precision, recall, or F1-score.

## F

**F-score**

F-score is a measure that combines precision and recall to evaluate the performance of a classification model.

**Factor analysis**

Factor analysis is a statistical method used to identify latent factors or underlying dimensions in a dataset and explain the relationships between observed variables.

**False negative**

False negative is a type of prediction error in binary classification in which a positive case is incorrectly classified as negative.

**False positive**

False positive is a binary classification prediction error in which a negative case is incorrectly classified as positive.

**Feature engineering**

Feature engineering is the process of using domain knowledge to create new input features for machine learning models. It involves transforming raw data into meaningful attributes that enhance model performance.

**Feature hashing**

Feature hashing is a technique used to convert categorical features into a numerical representation by applying a hash function.

**Feature reduction**

Feature reduction is the process of reducing the number of features or variables in a dataset while preserving relevant information and minimizing redundancy.

**Feature selection**

Feature selection is the process of selecting a subset of relevant features or variables from a larger set to build more interpretable and efficient models.

**Few-shot learning**

Few-shot learning is a machine learning approach that aims to learn new concepts or classes with limited training data or few examples.

**Float**

A float is a data type that represents floating-point numbers or decimal numbers with fractional parts.

**Flow variable**

A flow variable is used to propagate node parameters and settings from one node to another within a data processing workflow or pipeline.

**Fourier transform**

The Fourier transform is a mathematical technique that converts a function or signal into its constituent frequencies, enabling analysis in the frequency domain.

**Frequentist statistics**

Frequentist statistics is a statistical framework that focuses on the frequencies of events or outcomes based on repeated trials or observations.

**Front end**

The front end is the part of a software system or application that interacts directly with users and provides the user interface.

**Fuzzy algorithms**

Fuzzy algorithms are computational procedures that leverage fuzzy logic and approximation techniques to manage uncertainty and imprecision within data processing or decision-making tasks.

**Fuzzy c-means**

Fuzzy c-means is a clustering algorithm based on fuzzy logic that assigns data points to multiple clusters with varying degrees of membership.

**Fuzzy logic**

Fuzzy logic is a branch of logic that allows for degrees of truth rather than strict true or false values, incorporating uncertainty and ambiguity.

## G

**Gated recurrent unit**

A gated recurrent unit (GRU) is a type of recurrent neural network (RNN) architecture that uses gating mechanisms to selectively update and forget information in sequence modeling tasks.

**Gaussian distribution**

The Gaussian distribution, also known as the normal distribution, is a symmetric probability distribution with a bell-shaped curve defined by its mean and standard deviation.

**Geospatial analytics**

Geospatial analytics is the practice of analyzing and interpreting geographic or spatial data to uncover insights, identify patterns, and comprehend relationships within the physical environment.

**Goodness of fit**

Goodness of fit is a statistical measure that evaluates how well an observed data distribution matches an expected distribution or model.

**Gradient descent**

Gradient descent is an optimization algorithm used to minimize the loss function in machine learning models. It iteratively adjusts model parameters to find the best fit for the data.

**Greedy algorithms**

Greedy algorithms are any algorithms that make locally optimal choices at each stage, aiming to achieve a global optimum in optimization problems.

## H

**Hadoop**

Hadoop is an open-source framework that allows for distributed processing and storage of large datasets across multiple computing nodes using a cluster of commodity hardware.

**Heatmap**

A heatmap is a graphical representation of data where colors indicate the intensity or density of values in a matrix or grid.

**Hidden Markov model**

A hidden Markov model (HMM) is a probabilistic model used to model sequential data, assuming that the system being modeled is a Markov process with unobservable states.

**Hierarchical clustering**

Hierarchical clustering is a clustering technique that progressively joins proximate data points into clusters, resulting in a hierarchy of clusters based on the distance between them.

**Histogram**

A histogram is a graphical representation of the distribution of numerical data, dividing the data into bins or intervals and showing the frequency of values in each bin as a bar.

**Holdout sample**

A holdout sample is a subset of data set aside from the training data to evaluate the model's performance on unseen data.

**Holt-Winters forecasting**

Holt-Winters forecasting is a time series forecasting method that applies exponential smoothing to capture trends and seasonality.

**Human-in-the-loop**

Human-in-the-loop refers to systems in which human input is integrated into the machine learning process, often to improve model performance or ensure ethical considerations.

**Hyperparameter**

A hyperparameter is a parameter whose value is set before the learning process begins, controlling the behavior of the training algorithm.

**Hyperparameter tuning**

Hyperparameter tuning is the process of selecting the best parameters for a machine learning model. These parameters, set before training, control the learning process and model complexity.

**Hyperplane**

A hyperplane is a flat affine subspace of a higher-dimensional space, commonly used in machine learning for separating data points in classification tasks. In n-dimensional space, a hyperplane is a subspace of dimension n-1.

**Hypothesis**

A hypothesis is a proposed explanation made on the basis of limited evidence, serving as a starting point for further investigation. In data science, hypotheses are often tested through statistical methods to validate assumptions about data.

## I

**Imputation**

Imputation is the process of replacing missing data with substituted values. This technique helps in maintaining the dataset's completeness and is essential for accurate data analysis and model training.

**Inferential statistics**

Inferential statistics is a mathematical process that involves methods to make predictions or inferences about a population based on a sample of data. It includes hypothesis testing, confidence intervals, and regression analysis.

**Independent variable**

An independent variable is a variable that is manipulated or categorized to observe its effect on a dependent variable. It is the presumed cause in a cause-and-effect relationship.

**Integer**

An integer is a whole number that can be positive, negative, or zero. Integers are used in data science for various purposes, including indexing and categorical data representation.

**Interquartile range**

An interquartile range (IQR) is a measure of statistical dispersion and is calculated as the difference between the third quartile (Q3) and the first quartile (Q1). It is used to describe the spread of the middle 50% of a dataset.

**Iteration**

Iteration is the process of repeating a set of operations until a specific condition is met. In data science, iterations are used in algorithms and model training to progressively improve performance.

## J

**Joint probability**

Joint probability is the probability of two events occurring simultaneously. It is a key concept in probability theory and statistics and is useful in understanding relationships between variables.

**Julia**

Julia is a high-level, high-performance programming language designed for technical computing. It is particularly popular in data science for its speed and ease of use in numerical analysis and computational science.

## K

**Keras**

Keras is an open-source Python neural network library, used for creating and experimenting with deep learning models. It acts as an interface for the TensorFlow library.

**K-means**

K-means is a clustering algorithm that partitions data into *k* distinct clusters based on similarity. It minimizes the variance within each cluster and is widely used for data segmentation.

**K-nearest neighbors**

K-nearest neighbors (KNN) is a simple, supervised machine learning algorithm used for classification and regression. It assigns a class based on the majority vote of the k-nearest data points in the feature space.

**Kurtosis**

Kurtosis is a measure of the tailedness of the probability distribution in a real-valued random variable. High kurtosis indicates heavy tails, while low kurtosis indicates light tails relative to a normal distribution.

## L

**Labeled data**

Labeled data are datasets that have been tagged with one or more labels identifying the target or outcome. This data is essential for training supervised learning models.

**Lasso regression**

Lasso regression is a type of linear regression that includes a penalty term to enforce sparsity in the model coefficients. It helps in feature selection by shrinking less important feature coefficients to zero.

**Line chart**

A line chart is a type of data visualization that displays information as a series of data points called “markers” connected by straight-line segments. It is commonly used to track changes over intervals of time.

**Linear regression**

Linear regression is a statistical method for predicting one value based on other related values. It works by finding the best straight line that fits through the data points.

**Log likelihood**

Log likelihood is the natural logarithm of the likelihood function. It assesses the probability of observed data given parameter values and is crucial for maximizing numerical stability during estimation.

**Log loss**

Log loss, or logistic loss, is a performance metric for evaluating the accuracy of a classification model. It quantifies the uncertainty of the model's predictions by penalizing false classifications more heavily, with lower log loss values indicating better model performance.

**Logistic regression**

Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is usually binary (0 or 1).

**Long short-term memory **

Long short-term memory (LSTM) is a type of recurrent neural network (RNN) architecture used in deep learning. It is designed to model sequences and capture long-term dependencies, making it effective for tasks like time series prediction.

**Loops**

Loops are repetitive actions that repeatedly execute a block of code or a workflow snippet as long as a specified condition is met. They are fundamental in automating repetitive tasks.

## M

**Machine learning**

Machine learning is a branch of artificial intelligence that enables systems to learn from data and improve from experience without being explicitly programmed. It involves algorithms that can make predictions or decisions.

**MapReduce**

MapReduce is a programming model for processing and generating large datasets with a parallel, distributed algorithm on a cluster. It consists of a map step that filters and sorts data and a reduce step that performs a summary operation.

**Matplotlib**

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It is widely used for plotting data and creating graphs and charts.

**Market basket analysis**

Market basket analysis is a data mining technique used to discover associations between products purchased together. It is commonly used in retail to understand customer purchasing behavior.

**Market mix modeling**

Market mix modeling is a statistical analysis technique used to estimate the impact of various marketing tactics on sales and to forecast the impact of future marketing strategies.

**Maximum likelihood estimation**

Maximum likelihood estimation (MLE) is a method used to estimate the parameters of a statistical model. It finds the parameter values that maximize the likelihood of the observed data given the model.

**Mean**

The mean is the arithmetic average of a set of numbers, calculated by summing all the values and dividing by the count. It is a measure of central tendency in data.

**Mean (average, expected value)**

The mean, or expected value, is a measure of the central tendency of a probability distribution. It is the weighted average of all possible values that a random variable can take on.

**Mean absolute error**

Mean absolute error (MAE) is a measure of prediction accuracy in regression analysis. It calculates the average absolute differences between predicted values and actual values.

**Mean squared error**

Mean squared error (MSE) is a measure of the quality of an estimator. It calculates the average squared differences between predicted values and actual values, penalizing larger errors more than smaller ones.

**Median**

The median is the middle value in a data set when the values are arranged in ascending or descending order. It is a robust measure of central tendency that is not affected by outliers.

**MLOps**

MLOps is a way to manage machine learning projects from start to finish. It combines machine learning work with software development practices to make sure AI models work well in real-world use.

**Mode**

The mode is the value that appears most frequently in a data set. It is a measure of central tendency that is useful for categorical data.

**Model selection**

Model selection is the process of choosing the most appropriate model from a set of candidate models for a given dataset. It involves evaluating model performance using criteria like cross-validation.

**Monte Carlo simulation**

Monte Carlo simulation is a computational technique that uses random sampling to obtain numerical results. It is used to model the probability of different outcomes in complex systems.

**Multi-class classification**

Multi-class classification is a type of classification task where the goal is to categorize instances into one of three or more classes. Common algorithms include decision trees, support vector machines (SVMs), and neural networks.

**Multivariate analysis**

Multivariate analysis is a process that involves examining multiple variables to understand relationships and effects among them. It includes techniques like multivariate regression, factor analysis, and multivariate analysis of variance (MANOVA).

**Multivariate regression**

Multivariate regression is an extension of linear regression that models the relationship between multiple independent variables and multiple dependent variables.

## N

**Naive Bayes**

Naive Bayes is a probabilistic classifier based on Bayes' theorem, assuming independence between predictors. It is highly effective for text classification tasks like spam detection.

**NaN**

NaN is an acronym that stands for "not a number," and represents undefined or unrepresentable numerical results in computing. It is commonly encountered in data cleaning and preprocessing.

**Natural language processing**

Natural language processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and human language. It involves tasks like speech recognition, text analysis, and language generation.

**Nominal variable**

A nominal variable is a categorical variable with no intrinsic ordering among its categories. Examples include gender, nationality, and color.

**Non-relational database**

A non-relational database, or NoSQL database, is designed to handle large volumes of unstructured or semi-structured data. It offers flexibility and scalability compared to traditional relational databases.

**Normal distribution**

Normal distribution is a continuous probability distribution characterized by its bell-shaped curve, symmetric about the mean. It is foundational in statistics for many inferential techniques.

**Normalization**

Normalization is the process of scaling individual data points to have a standard range, often between 0 and 1. It improves the performance of machine learning algorithms.

**NoSQL**

NoSQL is a class of database management systems that do not adhere to the traditional relational database model. They are designed for distributed data storage and horizontal scaling.

**Numeric prediction**

Numeric prediction is a process for predicting a numerical value based on input data. Techniques include regression analysis and time series forecasting.

**Null hypothesis**

Null hypothesis is a statement that there is no effect or relationship between variables. It serves as the default assumption that researchers aim to test against using statistical methods.

**Numpy**

Numpy is a fundamental package for scientific computing in Python. It provides support for arrays, matrices, and a collection of mathematical functions to operate on these data structures.

## O

**Open source**

Open source software is software with source code that anyone can inspect, modify, and enhance. It fosters collaborative development and innovation in the tech community.

**One-hot encoding**

One-hot encoding is a technique for converting categorical variables into a binary matrix. Each category value is represented as a one-hot vector, which improves compatibility with machine learning algorithms.

**One shot learning**

One shot learning is a model’s ability to learn information about a task from a single training example. It is particularly useful in scenarios with limited data availability.

**Ordinal variable**

An ordinal variable is a categorical variable with a clear ordering among its categories. Examples include education level, satisfaction rating, and income brackets.

**Outlier**

An outlier is a data point that significantly differs from other observations. Outliers can indicate anomalies in measurement or experimental errors, and they often require special handling in analysis.

**Overfitting**

Overfitting is a situation in which a model learns the training data too well, capturing noise and anomalies instead of the underlying pattern. This results in poor performance on new, unseen data.

## P

**Pandas**

Pandas is a powerful data manipulation and analysis library for Python. It provides data structures like DataFrames and series for handling structured data with ease.

**Parameters**

Parameters are variables in a model that are learned from the training data. They define the model's function and are adjusted during the training process to minimize error.

**Pattern recognition**

Pattern recognition is the process of identifying patterns and regularities in data. It is a fundamental aspect of machine learning and is used in various applications like image and speech recognition.

**Pearson correlation coefficient**

The Pearson correlation coefficient (PCC) is the measurement of the linear relationship between two variables. It ranges from -1 to 1, with 1 indicating a perfect positive relationship and -1 a perfect negative relationship.

**Pie chart**

A pie chart is a circular statistical graphic divided into slices to illustrate numerical proportions. Each slice represents a category's contribution to the whole.

**Plotly**

Plotly is an open-source graphing library that makes interactive, publication-quality graphs online. It supports a wide range of visualizations, including line charts, scatter plots, and 3D charts.

**Poisson distribution**

The Poisson distribution predicts how often rare events happen in a specific time or space, like the number of customer complaints a store might receive in a day.

**Polynomial regression**

Polynomial regression is a method to find patterns in data that aren't straight lines. It uses curved lines (like parabolas) to predict one value based on another, allowing for more complex relationships than simple straight-line predictions.

**Pre-trained model**

A pre-trained model is a machine learning model that has been previously trained on a large dataset and can be fine-tuned for specific tasks. It saves time and resources when training new models.

**Precision**

Precision is a metric used to evaluate the performance of a classification model. Also known as the positive predictive value, precision measures the accuracy of positive predictions as a proportion of true positives among all positive predictions.

**Predictive analytics**

Predictive analytics is a process that uses statistical techniques and machine learning algorithms to analyze current and historical data to make predictions about future events and trends.

**Predictive model**

A predictive model is an algorithm that forecasts future outcomes using historical data, helping businesses anticipate trends, optimize strategies, and make informed decisions through statistical techniques and machine learning.

**Predictor variable**

A predictor variable is an independent variable used in regression analysis to predict the outcome of the dependent variable. It is also known as an explanatory variable.

**Principal component analysis**

Principal component analysis (PCA) is a dimensionality reduction technique that transforms data into a set of orthogonal components. It is used to reduce the complexity of data while preserving its variance.

**Probability distribution**

A probability distribution is a statistical function that describes all the possible values and probabilities for a random variable within a given range. There are two types of probability distributions: continuous probability distribution and discrete probability distribution.

**Program**

A program is a set of instructions that a computer follows to perform a specific task. It is written in a programming language and executed by the computer's processor.

**Programming language**

A programming language is a formal system of instructions used to create software. It provides a structured way to communicate complex commands to computers, resulting in specific outputs or behaviors. Examples include Python, Java, and C++.

**P-value**

P-value is a measure of the strength of evidence against the null hypothesis in a statistical test. A lower p-value indicates stronger evidence in favor of the alternative hypothesis.

**Python**

Python is a high-level, interpreted programming language known for its readability and versatility. It is widely used in data science, web development, automation, and scientific computing.

**PyTorch**

PyTorch is an open-source machine learning library based on the Torch library. It is widely used for applications such as deep learning research and natural language processing.

## Q

**Quartile**

A quartile is a type of quantile that divides a ranked dataset into four equal parts. The first quartile (Q1) is the median of the lower half, and the third quartile (Q3) is the median of the upper half.

**Q-Q plot**

A Q-Q plot, or quantile-quantile plot, is a graphical tool to compare two probability distributions by plotting their quantiles against each other. It helps to assess whether a dataset follows a particular distribution.

## R

**R**

R is a programming language and environment commonly used for statistical computing and graphics. It provides a wide variety of statistical techniques and graphical capabilities.

**Random forest**

Random forest is an ensemble learning method used for classification and regression. It operates by constructing multiple decision trees and combining their outputs for more accurate predictions.

**Random sample**

A random sample is a subset of individuals chosen from a larger set where each individual has an equal chance of being selected. It helps in obtaining a representative sample for statistical analysis.

**Random variable**

A random variable is a numerical representation of possible outcomes from an unpredictable event or process. It can be discrete (finite outcomes) or continuous (infinite outcomes).

**Range**

The range is the difference between the maximum and minimum values in a dataset. It provides a measure of the spread or dispersion of the data.

**Recall**

Recall is a metric used to evaluate the performance of a classification model. Also known as sensitivity, recall measures the ability to identify all positive instances as a proportion of correctly predicted positives among all positives.

**Recommendation engine**

A recommendation engine is a system that suggests products, services, or information to users based on analysis of data. It is widely used in e-commerce, streaming services, and social media.

**Regression**

Regression is a statistical technique that models relationships between a dependent variable and one or more independent variables to predict outcomes or forecast trends.

**Regression spline**

Regression spline is a regression analysis technique that fits piecewise polynomial functions to data. It provides flexibility in modeling non-linear relationships.

**Regularization**

Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty to the loss function. Common regularization methods include lasso and ridge regression.

**Reinforcement learning**

Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions and receiving rewards. Its goal is to maximize cumulative rewards over time.

**Relational database**

A relational database is a type of database that stores data in tables with rows and columns. It uses SQL for querying and managing data and ensures data integrity through relationships.

**Retrieval augmented generation**

Retrieval augmented generation (RAG) is a hybrid approach in natural language processing that combines retrieval-based and generation-based methods. It retrieves relevant information to augment the generation of more accurate and informative responses.

**Resampling**

Resampling is the process of drawing repeated samples from a dataset to assess the variability of a statistic. Techniques include bootstrapping and cross-validation, which are used to estimate accuracy and model performance.

**Residuals**

Residuals are the differences between observed and predicted values in a regression analysis. They help in diagnosing model fit and identifying potential outliers.

**Response variable**

The response variable is the dependent variable in a regression analysis. It is the variable that the model aims to predict or explain based on the independent variables.

**Ridge regression**

Ridge regression is a type of linear regression that includes a penalty term to shrink model coefficients. It helps to prevent overfitting and multicollinearity.

**ROC-AUC**

ROC-AUC is an acronym that stands for receiver operating characteristic – area under the curve. It is a performance measurement for classification models, indicating the model's ability to distinguish between classes.

**ROC curve**

The ROC curve is a graphical representation of a classification model's performance. It plots the true positive rate against the false positive rate at various threshold settings.

**Root mean squared error**

Root mean squared error (RMSE) is a measure of the differences between predicted and observed values in a regression analysis. It calculates the square root of the average squared differences.

**Rotational invariance**

Rotational invariance is the property of an algorithm to remain effective regardless of the rotation of the input data. It is important in image and pattern recognition tasks.

## S

**Sample**

A sample is a subset of individuals or observations selected from a larger population. It is used to make inferences about the population without examining every member.

**Sampling error**

Sampling error is the error caused by observing a sample instead of the whole population. It reflects the difference between the sample statistic and the actual population parameter.

**Scatter plot**

A scatter plot is a type of data visualization that typically displays values for two variables in a set of data. It uses Cartesian coordinates to show the relationship between the variables.

**Scikit-Learn**

Scikit-Learn is an open-source machine learning library for Python. It provides simple and efficient tools for data mining and data analysis, including classification, regression, and clustering algorithms.

**Seaborn**

Seaborn is a Python visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

**Semi-supervised learning**

Semi-supervised learning is a type of machine learning that uses a combination of labeled and unlabeled data for training. It leverages the unlabeled data to improve model performance.

**Skewness**

Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable. Positive skewness indicates a distribution with a tail on the right, while negative skewness indicates a tail on the left.

**SMOTE**

SMOTE (synthetic minority over-sampling technique) is a technique for addressing class imbalance in datasets. It generates synthetic examples for the minority class to balance the class distribution.

**Spatial-temporal reasoning**

Spatial-temporal reasoning is logic and understanding about space (spatial) and time (temporal). It is an area of AI used in applications like video analysis, navigation systems, and environmental modeling.

**Spearman rank correlation**

Spearman rank correlation is a non-parametric measure of the strength and direction of association between two ranked variables. It assesses how well the relationship between variables can be described by a monotonic function.

**SQL**

Structured query language (SQL) is a standardized language for managing and manipulating relational databases. It provides commands for querying, updating, and managing data.

**Standard deviation**

Standard deviation is a measure of the dispersion of a dataset relative to its mean. It quantifies the amount of variation or spread in the data.

**Standard error**

Standard error is the standard deviation of the sampling distribution of a statistic, typically the mean. It measures the precision of the sample mean as an estimate of the population mean.

**Standardization**

Standardization is the process of scaling data to have a mean of zero and a standard deviation of one. It is essential for ensuring that features contribute equally to model performance.

**Statistics**

Statistics is the science of collecting, analyzing, interpreting, and presenting data. It encompasses a wide range of techniques for making inferences about populations based on sample data.

**Stratified sampling**

Stratified sampling is a process that involves dividing a population into subgroups (strata) and taking a random sample from each stratum. It ensures that different segments of the population are adequately represented.

**Stochastic gradient descent**

Stochastic gradient descent (SGD) is an iterative optimization algorithm used for minimizing an objective function. It updates model parameters incrementally using a randomly selected subset of the data.

**String**

A string is a sequence of characters used to represent text. In programming, strings are a common data type used for storing and manipulating text.

**Structured data**

Structured data is data that adheres to a predefined format, making it easy to search, organize, and analyze. Examples include data in relational databases and spreadsheets.

**Summary statistics**

Summary statistics are descriptive statistics that quantitatively describe the main features of a dataset. They include measures like mean, median, mode, standard deviation, and range.

**Sunburst chart**

A sunburst chart is a visualization that represents hierarchical data using concentric circles. Each level of the hierarchy is represented by a ring, with the central circle representing the root.

**Supervised learning**

Supervised learning is a type of machine learning where the model is trained on labeled data. The goal is to learn a mapping from inputs to outputs, allowing the model to make predictions on new, unseen data.

**Support vector machine**

Support vector machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It works by finding the hyperplane that best separates different classes in the feature space.

**Synthetic data**

Synthetic data is artificially generated data that mimics the statistical properties of real-world data. It is used for testing and training machine learning models when real data is scarce or sensitive.

## T

**TensorFlow**

TensorFlow is an open-source machine learning framework developed by Google. It is widely used for building and deploying deep learning models.

**Time series analysis**

Time series analysis involves analyzing data points collected or recorded at specific time intervals. It is used to identify patterns, trends, and seasonal variations in time-dependent data.

**Tokenization**

Tokenization is the process of breaking text into individual units called tokens, typically words or phrases. It is a fundamental step in natural language processing tasks.

**Training and testing**

Training and testing are stages in the machine learning workflow. The training phase involves fitting a model to a dataset, while the testing phase evaluates the model's performance on new, unseen data.

**Transfer learning**

Transfer learning is a machine learning technique that applies knowledge from a pre-trained model to a new, related task, accelerating learning and improving performance. It is useful when data for the second task is limited.

**True negative**

A true negative is an outcome where the model correctly predicts the absence of a condition. It is used to evaluate the performance of classification models.

**True positive**

A true positive is an outcome where the model correctly predicts the presence of a condition. It is a key metric for assessing the accuracy of classification models.

**T-test**

A t-test is a statistical test used to determine if there is a significant difference between the means of two groups. It is commonly used in hypothesis testing.

**Type I error**

A type I error is a statistical error that occurs when a true null hypothesis is incorrectly rejected. It is also known as a false positive, indicating that an effect is detected when none exists.

**Type II error**

A type II error is a statistical error that occurs when a false null hypothesis is not rejected. It is also known as a false negative, indicating that an effect is missed when it is actually present.

## U

**Underfitting**

Underfitting is a data science scenario that occurs when a model is too simple to capture the underlying pattern in the data. It results in poor performance on both training and testing data.

**Univariate analysis**

Univariate analysis is a process that involves analyzing a single variable to summarize and find patterns. Techniques include calculating summary statistics and visualizing the data with histograms or box plots.

**Unstructured data**

Unstructured data is data that does not have a predefined format or organization. Examples include text, images, and audio files, which require special processing techniques to analyze.

**Unsupervised learning**

Unsupervised learning is a type of machine learning where the model is trained on unlabeled data. The goal is to discover hidden patterns or intrinsic structures within the data.

**UDF**

A user-defined function (UDF) is a custom function created by users to perform specific tasks not covered by standard functions in software or programming languages. In data science, UDFs let professionals tailor data analysis and manipulation to their specific needs.

## V

**Variance**

Variance is a measure of the dispersion of a set of data points around their mean value. It indicates how much the values differ from the mean.

**Vega Altair**

Vega-Altair is a Python library for declarative statistical visualization. It enables easy creation of interactive and informative data graphics.

**Violin plot**

A violin plot is a data visualization that combines aspects of box plots and density plots. It shows the distribution of the data across different categories.

## W

**Web scraping**

Web scraping is the process of extracting data from websites. It involves fetching web pages and parsing the content to collect structured information.

## X

**XGBoost**

XGBoost is an optimized gradient-boosting framework for machine learning. It is designed for speed and performance and is widely used in data science.

## Z

**Z-test**

A z-test is a statistical test used to determine if there is a significant difference between sample and population means. It is used when the sample size is large, and the population variance is known.

**Z-score**

A z-score is a measurement in statistics that shows the number of standard deviations a data point is from the mean. It is used to standardize data and identify outliers.

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