Data Science Glossary

Accuracy refers to the closeness of a measured value to a standard or known value. In the context of artificial intelligence, it often denotes the degree of correctness of a model's predictions compared to the actual outcomes.

An activation function is a mathematical operation applied to the output of a neural network node. It introduces non-linearity to the model, enabling it to learn complex patterns and make nonlinear transformations of the input data.

An algorithm is a step-by-step procedure or formula for solving a problem or accomplishing a task. In data science, algorithms are used to process and analyze data to extract meaningful insights or make predictions.

Apache Spark is an open-source distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. It is commonly used in big data processing and analytics applications.

API, or Application Programming Interface, is a set of rules, protocols, and tools for building software and applications. In data science, APIs are often used to access and interact with data from various sources or to integrate different software systems.

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, problem-solving, perception, and language understanding.

An artificial neural network (ANN) is a computational model inspired by the structure and functioning of biological neural networks. It consists of interconnected nodes (neurons) organized in layers, capable of learning and performing tasks such as classification and regression.

The back end refers to the server-side components of a software application responsible for processing data, managing databases, and executing business logic. In web development, the back end interacts with the front end to deliver dynamic content to users.

Backpropagation is a supervised learning algorithm used for training artificial neural networks. It involves updating the weights of the network's connections in reverse order, from the output layer back to the input layer, to minimize the difference between the predicted and actual outputs.

A Bayesian network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph. It is used to model uncertain knowledge and make predictions or decisions based on probabilistic inference.

Bayes' Theorem is a fundamental theorem in probability theory that describes the probability of an event based on prior knowledge or conditions related to the event. It is commonly used in Bayesian inference to update beliefs about hypotheses as new evidence becomes available.

In data science, bias refers to the systematic error introduced by the modeling process, resulting in predictions or estimations that deviate from the true values. It can arise from various sources, including sampling methods, model assumptions, or human judgment.

The balance between bias and variance in machine learning models to achieve optimal performance.

Large volumes of data, both structured and unstructured, that inundate a business on a day-to-day basis.

The binomial distribution is a discrete probability distribution that describes the number of successes in a fixed number of independent Bernoulli trials, each with the same probability of success. It is characterized by two parameters: the number of trials and the probability of success.

A business analyst is a professional who analyzes an organization's business domain and documents its processes or systems, assessing business models and integrating technology solutions to improve efficiency and achieve strategic goals.

Business analytics refers to the practice of using data analysis and statistical methods to analyze business information and make informed decisions. It involves interpreting data trends, forecasting future outcomes, and identifying opportunities for optimization or improvement.

Business intelligence is a technology-driven process for analyzing data and presenting actionable information to help executives, managers, and other corporate end users make informed business decisions. BI tools and systems collect and analyze data from various sources to support decision-making.

A categorical variable is a type of variable used in statistics that represents qualitative data with distinct categories or groups. Examples include gender, nationality, or product type. Categorical variables can be further classified as nominal or ordinal based on the nature of the categories.

Categorical variable classification is a process in data analysis and machine learning where categorical variables are used as input features to classify or predict the class or category of a target variable. It involves training classification models using categorical input variables and evaluating their performance.

The process of categorizing data points into classes or categories.

The process of grouping similar data points together.

Computer vision is a field of artificial intelligence and computer science that focuses on enabling computers to interpret and understand visual information from the real world. It involves developing algorithms and techniques for tasks such as image recognition, object detection, and scene understanding.

A confusion matrix is a table that visualizes the performance of a classification model by comparing actual and predicted values. It summarizes the number of true positives, true negatives, false positives, and false negatives, enabling the calculation of various performance metrics such as accuracy, precision, recall, and F1 score.

A continuous variable is a type of variable used in statistics that can take any value within a given range, typically represented by real numbers. Examples include height, weight, temperature, or time. Continuous variables are often measured rather than counted and can have an infinite number of possible values.

Correlation is a statistical measure that describes the strength and direction of a relationship between two or more variables. It is often expressed as a correlation coefficient, which ranges from -1 to 1. A positive correlation indicates a direct relationship, while a negative correlation indicates an inverse relationship.

A cost function, also known as a loss function, is a mathematical function used in optimization algorithms to quantify the difference between predicted and actual values. It measures the error or penalty associated with the model's predictions and is minimized during the training process to improve the model's performance.

Covariance is a statistical measure that describes the extent to which two random variables change together. It indicates the direction of the linear relationship between variables and whether they tend to move in the same direction (positive covariance) or opposite directions (negative covariance).

Cross-validation is a technique used to assess the performance and generalization ability of a predictive model. It involves partitioning the dataset into multiple subsets, training the model on a portion of the data, and evaluating its performance on the remaining data. Cross-validation helps estimate how well the model will perform on unseen data and assess its robustness.

A dashboard is a visual display of key performance indicators (KPIs), metrics, and data points that provide a snapshot of the current status or performance of a business, process, or system. Dashboards are often used to monitor trends, track progress, and make data-driven decisions.

Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It involves applying statistical, mathematical, and computational techniques to analyze datasets and extract insights.

A data analyst is a professional who interprets and analyzes data to generate insights and inform decision-making. They are skilled in data visualization, statistical analysis, and data mining techniques, and they often work with large datasets to identify trends, patterns, and correlations.

A database is a structured collection of data organized and stored in a computer system. It allows for efficient data storage, retrieval, and manipulation, typically using a database management system (DBMS). Databases can be relational, NoSQL, or hierarchical, depending on their structure and use cases.

A database management system (DBMS) is software that enables users to create, manage, and interact with databases. It provides tools and utilities for storing, retrieving, updating, and securing data, as well as managing database transactions and ensuring data integrity.

A data consumer is an individual or system that utilizes data for analysis, decision-making, or other purposes. This can include business users, analysts, data scientists, or automated processes that consume data from various sources to derive insights or drive actions.

A data engineer is a professional responsible for designing, building, and maintaining the infrastructure and architecture for data generation, storage, and processing. They develop data pipelines, ETL (extract, transform, load) processes, and data warehouses to support data-driven applications and analytics.

Data engineering is the discipline that focuses on designing, building, and maintaining systems for collecting, storing, and processing data. It involves implementing data pipelines, data warehouses, and ETL (extract, transform, load) processes to enable data-driven decision-making and analytics.

Data enrichment is the process of enhancing or augmenting raw data with additional information to improve its quality, relevance, or usefulness. This can involve adding metadata, geolocation data, or demographic information to existing datasets, enabling better analysis and insights.

Data exploration is the initial phase of data analysis where analysts or data scientists explore and familiarize themselves with a dataset. It involves summarizing the main characteristics of the data, identifying patterns or trends, and generating hypotheses for further analysis.

A dataframe is a two-dimensional labeled data structure commonly used in data analysis and manipulation. It resembles a table with rows and columns, where each column can contain different types of data. Dataframes are widely utilized in programming languages such as Python (Pandas) and R for handling structured data.

Data governance refers to the framework, processes, and policies organizations implement to ensure data quality, integrity, security, and compliance throughout the data lifecycle. It involves defining roles and responsibilities, establishing data standards, and enforcing regulations to maximize the value and trustworthiness of data assets.

Data journalism is a practice that involves the use of data analysis and visualization techniques to uncover, tell, and contextualize news stories. It combines investigative journalism with data-driven insights to produce compelling narratives and uncover trends, patterns, or anomalies in large datasets.

A data lake is a centralized repository that stores structured, semi-structured, and unstructured data at scale. Unlike traditional data warehouses, data lakes allow organizations to store raw, unprocessed data from various sources without predefined schemas, enabling flexible data exploration, analytics, and machine learning.

Data literacy refers to the ability to understand, analyze, interpret, and communicate insights from data effectively. It involves not only technical skills in data analysis and visualization but also critical thinking, domain knowledge, and ethical considerations to make informed decisions based on data.

Data mining is the process of discovering patterns, correlations, or insights from large datasets using techniques from statistics, machine learning, and database systems. It involves extracting valuable knowledge from data to support decision-making, prediction, or optimization in various domains.

Data modeling is the process of designing and structuring data in a way that facilitates efficient storage, retrieval, and analysis. It involves defining data entities, relationships, and attributes to create conceptual, logical, and physical models that accurately represent the underlying data and support business requirements.

A data pipeline is a series of processes and tools used to extract, transform, and load (ETL) data from various sources to a destination such as a data warehouse, data lake, or analytics platform. It automates the flow of data, ensuring consistency, reliability, and scalability in data processing workflows.

Data science is an interdisciplinary field that combines domain knowledge, programming skills, and statistical techniques to extract insights and knowledge from data. It encompasses various activities such as data collection, cleaning, analysis, visualization, and interpretation to inform decision-making and solve complex problems.

A data scientist is a professional skilled in applying data science techniques and methodologies to analyze and interpret complex datasets. They possess expertise in statistics, programming, machine learning, and domain knowledge, enabling them to uncover patterns, derive insights, and build predictive models to solve business challenges.

A dataset is a structured collection of data that is typically organized into rows and columns, with each row representing an individual observation or record, and each column representing a specific variable or attribute. Datasets are used for analysis, modeling, and machine learning tasks.

A data structure is a specialized format for organizing, processing, and storing data in a computer system. Common data structures include arrays, linked lists, stacks, queues, trees, and graphs, each optimized for different operations and applications.

Data visualization is the graphical representation of data and information using visual elements such as charts, graphs, and maps. It aims to communicate insights and patterns in the data effectively, making it easier for users to understand and interpret complex datasets.

A data warehouse is a centralized repository that stores structured and unstructured data from multiple sources, typically for reporting and analysis purposes. It integrates data from various operational systems and provides a unified view of the organization's data for decision-making.

Data wrangling, also known as data munging, is the process of cleaning, transforming, and preparing raw data into a usable format for analysis or modeling. It involves tasks such as handling missing values, removing duplicates, standardizing data formats, and merging datasets.

A decision support tool that uses a tree-like graph of decisions and their possible consequences.

A subset of machine learning that utilizes artificial neural networks with multiple layers.

Dimensionality reduction is the process of reducing the number of input variables or features in a dataset while preserving its important information. It is commonly used in machine learning and data analysis to alleviate the curse of dimensionality, improve computational efficiency, and prevent overfitting.

EDA, or Exploratory Data Analysis, is a preliminary process in data analysis that focuses on understanding the structure, patterns, and relationships in a dataset through visual and statistical methods. EDA helps identify interesting features, detect anomalies, and formulate hypotheses for further analysis.

ELT, or Extract, Load, Transform, is a data integration process where data is first extracted from source systems, then loaded into a target system, and finally transformed or processed to meet the required format or structure. ELT is commonly used in data warehousing and big data environments.

ETL, or Extract, Transform, Load, is a data integration process where data is extracted from source systems, transformed into a standardized format, and loaded into a target system, such as a data warehouse or database. ETL is used to consolidate, clean, and organize data for analysis or reporting.

Evaluation metrics are measures used to assess the performance or effectiveness of a predictive model or algorithm. Common evaluation metrics include accuracy, precision, recall, F1 score, ROC curve, and confusion matrix, each providing insights into different aspects of model performance.

A false negative, also known as a Type II error, occurs when a diagnostic test or classifier incorrectly predicts a negative outcome when the true outcome is positive. In binary classification, false negatives represent instances where the model fails to detect a relevant condition or event.

A false positive, also known as a Type I error, occurs when a diagnostic test or classifier incorrectly predicts a positive outcome when the true outcome is negative. In binary classification, false positives represent instances where the model incorrectly identifies a condition or event that is not present.

In machine learning and data analysis, a feature refers to an individual measurable property or characteristic of a phenomenon being observed. Features are used as input variables in predictive models to represent patterns, relationships, or attributes of the data that influence the target variable.

The process of selecting, transforming, or creating new features from raw data to improve model performance.

Feature selection is the process of choosing a subset of relevant features from a larger set of input variables to improve the performance of a predictive model. It aims to reduce overfitting, improve model interpretability, and enhance computational efficiency by focusing on the most informative features.

The front end refers to the client-side components of a software application that users interact with directly. It encompasses the user interface, presentation layer, and user experience design, enabling users to interact with the application's functionality and access its features.

A measure of a test's accuracy that considers both precision and recall, calculated as the harmonic mean of precision and recall.

Fuzzy algorithms are computational techniques based on fuzzy logic that deal with uncertainty and imprecision in data or problem domains. They are designed to handle situations where traditional binary logic or crisp algorithms may not be suitable due to vagueness or ambiguity.

Fuzzy logic is a form of multi-valued logic that allows for degrees of truth instead of the strict true/false dichotomy of classical logic. It is used to model and reason about uncertainty and vagueness in data or decision-making processes, making it particularly useful in domains where linguistic terms and fuzzy concepts are prevalent.

An optimization algorithm used to minimize the loss function by adjusting the parameters of a model in the direction of the steepest descent of the gradient.

Greedy algorithms are problem-solving techniques that make locally optimal choices at each step with the hope of finding a global optimum solution. They are characterized by their greedy property, which means they make decisions based solely on the information available at the current stage without considering future consequences.

Hadoop is an open-source distributed processing framework designed for storing and processing large volumes of data across clusters of commodity hardware. It consists of the Hadoop Distributed File System (HDFS) for storage and the MapReduce programming model for processing and analyzing data in parallel.

Parameters that define the structure and behavior of a machine learning model, typically set before the learning process begins.

In statistics, a hypothesis is a proposed explanation or assertion about a phenomenon or relationship between variables. It typically involves stating a null hypothesis, which assumes no effect or relationship, and an alternative hypothesis, which suggests an effect or relationship that is being tested through statistical analysis.

Imputation is the process of replacing missing or incomplete data with estimated values based on other available information. It is commonly used in data preprocessing to handle missing data before analysis or modeling, helping to maintain the integrity and usability of the dataset.

K-Means is a popular clustering algorithm used in unsupervised machine learning to partition a dataset into K distinct clusters based on similarity or proximity between data points. It iteratively assigns each data point to the nearest cluster centroid and updates the centroids until convergence, optimizing the within-cluster sum of squares.

A non-parametric algorithm used for classification and regression tasks that relies on the similarity of data points in a feature space.

Linear algebra is a branch of mathematics that deals with vector spaces and linear mappings between these spaces, represented by systems of linear equations. It includes concepts such as vectors, matrices, determinants, eigenvalues, and eigenvectors, which are fundamental to many areas of mathematics, science, and engineering.

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. It aims to find the best-fitting line or hyperplane that minimizes the difference between the observed and predicted values.

Logistic regression is a statistical method used for binary classification tasks, where the dependent variable is categorical and has two possible outcomes. It models the probability of the binary outcome using a logistic function, which maps the input features to the probability of belonging to a particular class.

A subset of artificial intelligence focused on the development of algorithms and statistical models that enable computers to learn and improve from experience.

The mean, also known as the average, is a measure of central tendency that represents the arithmetic average of a set of values. It is calculated by summing all the values in the dataset and dividing by the total number of values.

Mean Absolute Error (MAE) is a metric used to evaluate the accuracy of a regression model by measuring the average absolute difference between the predicted and actual values. It provides a straightforward measure of the model's prediction error, where smaller values indicate better performance.

A measure of the average squared difference between predicted and actual values, commonly used to evaluate regression models.

The median is a measure of central tendency that represents the middle value of a dataset when arranged in ascending or descending order. It divides the dataset into two equal halves, with half of the values falling below and half above the median.

The mode is a measure of central tendency that represents the most frequently occurring value in a dataset. Unlike the mean and median, which may not be unique, the mode is the value with the highest frequency.

Model tuning, also known as hyperparameter tuning, is the process of optimizing the performance of a machine learning model by selecting the best set of hyperparameters. It involves systematically adjusting hyperparameters, such as learning rate, regularization strength, or tree depth, and evaluating the model's performance using cross-validation or other techniques.

Multivariate modeling is a statistical analysis technique that involves modeling the relationship between multiple independent variables and a single dependent variable. It extends the concept of univariate modeling, which deals with only one independent variable, to capture complex interactions and dependencies among multiple variables.

Naive Bayes is a probabilistic classification algorithm based on Bayes' theorem and the assumption of independence between features. It calculates the probability of each class given a set of input features and predicts the class with the highest probability. Despite its simplicity and the naive assumption, Naive Bayes is effective in many real-world classification tasks.

A branch of artificial intelligence that focuses on the interaction between computers and humans through natural language.

Normalization is the process of rescaling numerical data to a standard range, typically between 0 and 1, to remove the effects of scale and ensure that all features contribute equally to analysis or modeling. It is commonly used in data preprocessing to improve the stability and performance of machine learning algorithms.

To normalize means to transform data so that it conforms to a standard scale or distribution, often by rescaling it to have a mean of zero and a standard deviation of one. Normalization is used to facilitate comparisons and analyses by removing the effects of scale and ensuring consistency across different datasets or variables.

NoSQL, or Not Only SQL, is a broad category of database management systems that provide flexible data models for storage and retrieval of unstructured or semi-structured data. Unlike traditional relational databases, NoSQL databases are designed to scale horizontally and handle large volumes of data with high performance and availability.

In statistical hypothesis testing, the null hypothesis is a statement that there is no significant difference or effect between the variables being compared. It serves as the default assumption to be tested against an alternative hypothesis, and the goal is to determine whether there is enough evidence to reject or fail to reject the null hypothesis.

Open source refers to software or projects that provide source code freely available for anyone to use, modify, or distribute under an open-source license. Open-source software fosters collaboration, transparency, and innovation by allowing developers to contribute to and improve upon existing codebases.

An ordinal variable is a type of categorical variable with ordered or ranked categories that have a meaningful sequence or hierarchy. Unlike nominal variables, where categories have no inherent order, ordinal variables convey information about relative differences or preferences between categories, but the intervals between categories may not be uniform.

An outlier is an observation or data point that significantly deviates from the rest of the dataset. Outliers may occur due to measurement errors, experimental variability, or genuine but rare events. They can affect the accuracy and reliability of statistical analyses and machine learning models and may need to be identified and addressed appropriately.

The phenomenon where a machine learning model learns to fit the training data too closely, leading to poor generalization and performance on unseen data.

In the context of machine learning models, a parameter refers to a configuration variable that is internal to the model and is learned from the training data. Parameters are used to define the structure and behavior of the model and are adjusted during the training process to minimize the discrepancy between the model's predictions and the actual outcomes. Examples of parameters include coefficients in linear regression, weights in neural networks, and centroids in clustering algorithms.

A metric that measures the proportion of true positive predictions among all positive predictions made by a model.

Predictive analytics is the practice of extracting insights from historical data to predict future trends, behaviors, or outcomes. It involves applying statistical algorithms, machine learning techniques, and data mining methods to analyze patterns in data and make informed predictions about future events or behaviors. Predictive analytics is used across various industries for forecasting, risk management, marketing optimization, and decision-making.

A dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional space while preserving the most important features or patterns.

Python is a high-level, interpreted programming language known for its simplicity, readability, and versatility. It is widely used in various domains, including web development, scientific computing, data analysis, artificial intelligence, and machine learning. Python's extensive standard library and third-party packages make it a popular choice for developing applications, scripting tasks, and building data-driven solutions.

Quantitative analysis is a method of analyzing numerical data using mathematical and statistical techniques to understand and interpret patterns, relationships, and trends. It involves collecting, processing, and analyzing quantitative data to derive insights, make predictions, and support decision-making in fields such as finance, economics, science, and engineering.

R is a programming language and environment specifically designed for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, time-series analysis, clustering, and data visualization. R is widely used in academia, research, and industry for data analysis, statistical modeling, and exploratory data analysis.

An ensemble learning method that constructs multiple decision trees during training and outputs the mode or mean prediction of the individual trees as the final prediction.

A metric that measures the proportion of actual positive cases that were correctly identified by a model out of all actual positive cases.

Regression analysis is a statistical method used to model the relationship between one or more independent variables (predictors) and a dependent variable (response). It aims to estimate the strength and direction of the relationship by fitting a mathematical function (regression model) to the observed data. Regression models can be linear or nonlinear and are commonly used for prediction, inference, and hypothesis testing in various fields, including economics, finance, biology, and social sciences.

A machine learning paradigm where an agent learns to make decisions by interacting with an environment, receiving feedback in the form of rewards or penalties.

A residual, also known as an error, is the difference between the observed value and the value predicted by a model. In regression analysis, residuals represent the discrepancy between the actual data points and the fitted regression line. Residuals are used to assess the goodness of fit of a model and identify patterns or trends that may indicate model inadequacies.

Root Mean Squared Error (RMSE) is a metric used to evaluate the accuracy of a regression model by measuring the average squared difference between the predicted and actual values, taking the square root of the result. RMSE provides a measure of the model's prediction error in the same units as the dependent variable, making it easy to interpret and compare across different datasets.

In statistics, a sample is a subset of individuals, observations, or data points selected from a larger population for the purpose of analysis or inference. Samples are used to estimate population parameters, test hypotheses, and make inferences about the characteristics of the population from which they are drawn.

Sampling error is the discrepancy between a sample statistic and the true population parameter it is intended to estimate. It arises due to the inherent variability in samples and can affect the accuracy and reliability of statistical estimates. Sampling error can be minimized by using appropriate sampling techniques and increasing the sample size.

SQL, or Structured Query Language, is a programming language used to manage and manipulate relational databases. It provides a standard syntax for querying, updating, and managing data stored in relational database management systems (RDBMS). SQL is widely used for data manipulation, retrieval, and administration in various applications and industries.

Standard deviation is a measure of the dispersion or variability of a dataset, representing the average distance of data points from the mean. It indicates the degree of spread or dispersion around the mean and provides insights into the consistency or variability of the data. A higher standard deviation indicates greater variability, while a lower standard deviation indicates more consistency.

Statistical significance is a measure of the likelihood that an observed difference or relationship in data is not due to random chance. It is determined through statistical hypothesis testing, where the null hypothesis is tested against an alternative hypothesis using appropriate statistical tests. A result is considered statistically significant if it is unlikely to have occurred by chance alone, typically with a predefined level of significance (e.g., p < 0.05).

Summary statistics are numerical summaries or descriptions of the key characteristics of a dataset, providing insights into its central tendency, dispersion, and shape. Common summary statistics include measures such as the mean, median, mode, standard deviation, range, and quartiles, which summarize different aspects of the data distribution.

A machine learning approach where models are trained on labeled data, with input-output pairs provided during the training process to learn the mapping between inputs and outputs.

Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. SVM constructs a hyperplane or set of hyperplanes in a high-dimensional space that separates data points into different classes or predicts continuous outcomes. It is effective for handling high-dimensional data and is particularly useful when the data is not linearly separable.

Synthetic data refers to artificially generated data that mimic the characteristics of real-world data but are not obtained from actual observations. Synthetic data can be generated using statistical models, simulation techniques, or generative adversarial networks (GANs) and are used for various purposes, including data augmentation, privacy protection, and model testing.

In supervised machine learning, the target variable, also known as the dependent variable or response variable, is the variable that the model seeks to predict based on the values of other variables, called predictor variables or features. The target variable represents the outcome or response of interest in a predictive modeling task and is used to train and evaluate the performance of machine learning algorithms.

A subset of data used to evaluate the performance of a machine learning model after it has been trained on a training set, helping to assess generalization and model quality.

A time series is a sequence of data points collected, recorded, or observed at successive time intervals, typically equidistant. Time series data is used to analyze and model the behavior of a variable over time, making it useful for forecasting future trends, detecting patterns, and understanding underlying dynamics. Time series analysis involves techniques such as trend analysis, seasonal decomposition, and autoregressive modeling to extract meaningful insights from temporal data.

A subset of data used to train a machine learning model, consisting of input-output pairs that the model learns from during the training process.

True Negative (TN) is a term used in binary classification to represent the number of correctly identified negative instances or observations. It refers to cases where the model correctly predicts the absence of the target condition or event.

True Positive (TP) is a term used in binary classification to represent the number of correctly identified positive instances or observations. It refers to cases where the model correctly predicts the presence of the target condition or event.

Underfitting occurs when a machine learning model is too simple to capture the underlying patterns or relationships in the data. It typically results in poor performance on both the training and test datasets, as the model fails to adequately represent the complexity of the data. Underfitting can be addressed by increasing the model's complexity, adding more features, or using more sophisticated algorithms.

Univariate modeling is a statistical analysis technique that focuses on modeling the relationship between a single independent variable and a dependent variable. It involves examining the impact of one variable on another without considering additional predictors or covariates. Univariate modeling is useful for understanding the direct effects of individual variables and for simplifying complex relationships in data analysis.

Data that lacks a predefined data model or organization, often in the form of text, images, audio, or video, requiring specialized techniques for analysis and processing.

A machine learning approach where models learn patterns or structures from unlabeled data without explicit supervision, typically used for clustering, dimensionality reduction, or generative modeling.

Variance is a measure of the dispersion or spread of a dataset around its mean. It quantifies the average squared difference between each data point and the mean, providing insights into the variability or diversity of the data. A higher variance indicates greater dispersion, while a lower variance suggests more consistency or uniformity.

Web scraping is the automated process of extracting data from websites using software tools or scripts. It involves fetching and parsing HTML content from web pages, extracting relevant information, and storing it in a structured format, such as a database or spreadsheet. Web scraping is commonly used for gathering data for research, analysis, or business purposes.