A set of rules or instructions used to solve a specific problem or perform a specific task.

The process of examining data to uncover insights and make data-driven decisions.

A set of protocols and tools that allows different software applications to communicate with each other.

Extremely large and complex datasets that cannot be easily managed with traditional data processing tools.

Technologies, applications, and practices for the collection, integration, analysis, and presentation of business information.

The process of grouping similar data points together based on their characteristics or attributes.

A simple file format that stores tabular data (numbers and text) as plain text, with each line representing a data record and each field separated by commas.

The process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in datasets.

The process of extracting useful patterns and knowledge from large datasets.

The process of creating a representation of the structure and relationships within a dataset.

The process of extracting data from various sources, transforming it into a consistent format, and loading it into a data warehouse or target system.

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

The process of transforming raw data into features that can be used to improve the performance of machine learning models.

The process of making predictions or estimates about future events or trends based on historical data.

A database that uses graph structures to represent and store data, where nodes represent entities and edges represent relationships between them.

In data manipulation, the groupby operation is used to split data into groups based on specified criteria and then perform computations or analyses on each group independently.

An open-source framework for processing and storing large datasets in a distributed computing environment.

A statistical method used to test the validity of assumptions or claims about a population based on sample data.

The process of drawing conclusions or making predictions based on observed data.

The network of physical objects embedded with sensors, software, and connectivity to collect and exchange data.

A lightweight data interchange format that is easy for humans to read and write and easy for machines to parse and generate.

In database management, a join operation combines rows from two or more tables based on a related column to create a new result set containing data from both tables.

A type of NoSQL database that stores data as key-value pairs, where each key is associated with a single value, allowing for efficient data retrieval.

A popular algorithm for partitioning a dataset into k distinct non-overlapping clusters.

A statistical method used to model the relationship between a dependent variable and one or more independent variables, assuming a linear relationship between them.

A classification algorithm used to model the probability of a binary outcome, where the output is mapped to the logistic function to obtain the probability values.

Data that provides information about other data, such as the structure, format, and context of datasets.

The average value of a set of numbers calculated by summing all values and dividing by the total number of values.

The middle value in a set of numbers when they are arranged in ascending or descending order.

The value that appears most frequently in a set of numbers.

A field of study that uses algorithms and statistical models to enable computers to learn from and make predictions or decisions based on data.

The process of assessing the performance and effectiveness of a machine learning model.

A category of databases that use a variety of data models, including key-value pairs, document-based, column-family, and graph-based data structures for storage and retrieval, rather than traditional relational database models.

In data analysis, noise refers to random variations or errors present in the data that can obscure or distort underlying patterns.

Also known as the Gaussian distribution, it is a continuous probability distribution commonly encountered in statistical analysis and modeling.

In hypothesis testing, the null hypothesis represents the default assumption that there is no significant effect or difference between groups or variables.

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

The process of identifying and removing data points that deviate significantly from the majority of the data, potentially indicating errors or anomalies.

A phenomenon in machine learning where a model learns to perform well on the training data but fails to generalize to new, unseen data, due to capturing noise and irrelevant patterns.

The process of finding the best solution or values for the parameters of a model to achieve the desired outcome, often involving minimizing or maximizing an objective function.

The use of data, statistical algorithms, and machine learning techniques to identify patterns and make predictions about future events or outcomes.

A measure used in statistical hypothesis testing to determine the probability of obtaining results as extreme as the observed data, assuming that the null hypothesis is true.

A mathematical function that describes the likelihood of different outcomes occurring in a random event or variable, often represented as a histogram or continuous curve.

A request for information or data retrieval from a database or information system using a specific search condition or criterion.

Data that consists of numerical measurements or values, representing quantities that can be measured and compared mathematically.

Data that represents attributes, characteristics, or categories that cannot be expressed numerically, often described using labels or categories.

A statistical method used to determine the relationship between a dependent variable and one or more independent variables.

A type of database that organizes data into one or more tables, with relationships between the tables defined by common attributes.

A type of machine learning where an agent interacts with an environment, learning to take actions to maximize rewards or achieve specific goals.

A programming language used to manage and manipulate relational databases, allowing users to retrieve, insert, update, and delete data.

A measure of the amount of variation or dispersion in a set of data values, representing the average deviation from the mean.

A type of machine learning where a model is trained on labeled data, with input-output pairs provided, to make predictions or decisions.

The process of deriving useful information or patterns from unstructured text data.

The analysis of data collected at regular intervals over time to identify patterns, trends, and seasonality.

The dependent variable in a statistical or machine learning model that is being predicted or explained by the independent variables.

Data that does not have a predefined data model or organization, such as text, images, audio, or video, and requires special processing techniques for analysis.

A probability distribution where all outcomes have equal probabilities, forming a constant probability density function over a specified interval.

A type of machine learning where a model is trained on unlabeled data, without specific input-output pairs, to find patterns or structures in the data.

A statistical measure that quantifies the dispersion or spread of a set of data points around the mean, indicating how much the data values deviate from the average.

The representation of data or information through visual means, such as charts, graphs, or dashboards.

The process of evaluating and testing the performance, accuracy, and effectiveness of models, algorithms, or systems using separate datasets.

An average where each data point or value is given a specific weight based on its importance or significance. The weight reflects the relative contribution of each data point to the overall average, and the weighted average takes into account both the values and their corresponding weights.

A visual representation of textual data, where words are displayed in different sizes and colors based on their frequency in the text, used to highlight important terms or topics.

The automated process of extracting data from websites. It involves using web crawlers or bots to navigate web pages, access the underlying HTML or XML code, and extract specific data elements for analysis or storage in a structured format.

A technique in natural language processing that maps words or phrases into continuous vector representations, enabling semantic relationships between words to be captured in numerical space.

The process of collecting, analyzing, and interpreting data related to website usage and user behavior. It involves tracking and measuring website traffic, user interactions, conversion rates, and other key performance indicators (KPIs) to gain insights and make data-driven decisions to optimize website performance.

The sequence of tasks or steps involved in a data analysis or data processing pipeline.

In a two-dimensional coordinate system, the X-axis represents the horizontal axis. It is used to plot and display values of the independent variable in a graph or chart.

A markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable. It uses tags to define elements and attributes to describe the data. XML is widely used for data exchange between different systems and for representing hierarchical data structures.

Cross-validation, often abbreviated as “X-validation,” is a model evaluation technique used to assess the performance of machine learning models. The dataset is divided into multiple subsets (folds), and the model is trained and tested on different combinations of training and validation sets to provide a more robust estimate of the model’s performance.

In a two-dimensional coordinate system, the Y-axis represents the vertical axis. It is used to plot and display values of the dependent variable or response variable in a graph or chart.

A human-readable data serialization format often used for configuration files in software applications. It uses indentation to represent data hierarchies and supports various data types, making it easy for both humans and machines to read and write.

A statistical measure that indicates how many standard deviations a data point is from the mean of the dataset. It is used to standardize and compare values from different distributions.

A condition where there is no systematic error or bias in a measurement or analysis, indicating that the data or results are accurate and unbiased.

In game theory, a Zero-Sum Game is a situation where one player’s gain is exactly balanced by another player’s loss, resulting in a net gain of zero.

A statistical test used to compare a sample mean to a known population mean when the population standard deviation is known.