FBLA Data Science and Ai Vocabulary

Probability and Statistics

The mathematical study of random events, data distributions, and likelihood used to model uncertainty in data science.

Foundations of Data Science

The core interdisciplinary field combining statistics, data analysis, scientific methods, and algorithms to extract knowledge from data.

Data Analysis and Statistics for AI

The process of inspecting, cleaning, transforming, and modeling data to discover useful information and train machine learning models.

Tools for Data and AI

Software applications, programming languages (such as Python or R), libraries, and cloud platforms used to build and deploy AI models.

AI Basics

Fundamental concepts of artificial intelligence, including agent behavior, task environments, and automated reasoning.

Machine Learning

A branch of AI focused on building applications that learn from data and improve their accuracy over time without being explicitly programmed.

Reasoning

The cognitive or computational process of drawing logical conclusions and inferences from available premises or data.

Perception, Representation, and AI

How AI systems process sensory inputs (like vision or speech) and structure that information internally to understand the world.

Privacy and Ethics in AI

The study of moral issues, fairness, accountability, transparency, and data protection surrounding AI deployment and data collection.

Data Literacy

The ability to read, work with, analyze, and communicate effectively with data.

Algorithmic Bias

Systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.

Supervised Learning

A type of machine learning where the algorithm is trained on labeled data to learn the mapping function from inputs to outputs.

Unsupervised Learning

A type of machine learning used to find hidden patterns or intrinsic structures in unlabeled input data.

Neural Network

A series of algorithms that endeavors to mimic underlying relationships in a set of data through a process that mimics the operation of the human brain.

Big Data

Extremely large datasets that may be analyzed computationally to reveal patterns, trends, and associations.

Data Cleansing

The process of detecting and correcting (or removing) corrupt, inaccurate, or duplicate records from a record set or database.

Overfitting

A modeling error which occurs when a function is too closely fit to a limited set of data points, failing to generalize to broader datasets.

Natural Language Processing (NLP)

A branch of artificial intelligence that helps computers understand, interpret, and manipulate human language.

Computer Vision

An artificial intelligence field that trains computers to interpret and understand the visual world from digital images or videos.

Reinforcement Learning

An area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize cumulative reward.

Underfitting

A modeling error that occurs when a machine learning model is too simple to capture the underlying patterns in the training data.

Classification

A supervised learning task where the model predicts a discrete category or label for an input data point.

Regression

A supervised learning task used to predict a continuous numerical value based on input features.

Clustering

An unsupervised learning technique that groups similar data points together based on their characteristics.

Deep Learning

A subset of machine learning based on artificial neural networks with multiple layers (hence "deep") that process complex data representations.

Feature Engineering

The process of using domain knowledge to select, transform, and extract relevant variables (features) from raw data to improve machine learning model performance.

Data Wrangling

The process of cleaning, structuring, and enriching raw data into a desired format for better decision-making and analysis.

Training Data

The initial dataset used to teach a machine learning algorithm how to recognize patterns or make predictions.

Testing Data

An independent dataset used to evaluate the final performance and generalization ability of a trained machine learning model.

Cross-Validation

A resampling technique used to evaluate machine learning models on a limited data sample by partitioning data into training and validation subsets.

Decision Tree

A non-parametric supervised learning algorithm that uses a tree-like model of decisions and their possible consequences.

Random Forest

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

Support Vector Machine (SVM)

A supervised learning algorithm that finds a hyperplane in an N-dimensional space to distinctly classify data points.

K-Means Clustering

An iterative unsupervised algorithm that partitions a dataset into K distinct non-overlapping subgroups or clusters.

Gradient Descent

An optimization algorithm used to minimize a cost function by iteratively moving in the direction of steepest descent.

Loss Function

A method used to evaluate how well a specific algorithm models the given data by measuring the error between predicted and true values.

Data Pipeline

A set of actions that takes raw data and moves it through a series of processing steps to a destination where it can be analyzed.

Dimensionality Reduction

The process of reducing the number of random variables under consideration by obtaining a set of principal variables (e.g., PCA).

Generative AI

A type of artificial intelligence technology that can produce various types of content, including text, imagery, audio, and synthetic data.

Large Language Model (LLM)

An advanced AI model trained on massive text corpora using deep learning architectures to understand and generate human-like language.