Data Analytics (DA)

These terms encapsulate the fundamental techniques, processes, and challenges in the field of data analytics, highlighting how data is prepared, processed, analyzed, and visualized to derive meaningful insights and inform decision-making.

Accuracy - A measure of correctness in data analytics, reflecting the degree to which the results of an analysis match the true values or expected outcomes.

Anomaly Detection - The identification of unusual patterns or outliers in data that do not conform to expected behavior, crucial in many data analytics applications like fraud detection.

Association - A technique in data analytics used to discover patterns or relationships between variables in large datasets, often used in market basket analysis.

Big Data - Represents large, complex datasets that traditional data processing applications are inadequate to deal with, and is foundational to modern data analytics challenges and solutions.

Classification - A data analytics task that involves categorizing data into predefined groups or classes, making it a fundamental method in machine learning for data analysis.

Clustering - An unsupervised learning technique used in data analytics to group a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.

Data Cleaning - The process of detecting and correcting (or removing) corrupt or inaccurate records from a dataset, a critical initial step in the data analytics process.

Data Exploration - The initial step in data analysis, where users explore a large set of data to find initial patterns, characteristics, and points of interest without having a specific goal in mind.

Data Lake - A storage repository that holds a vast amount of raw data in its native format until it is needed, an important concept in managing the diverse and massive datasets used in data analytics.

Data Visualization - The graphical representation of information and data, a key technique in data analytics for communicating findings and insights in an accessible and intuitive manner.

Deep Learning (DL) - The use of deep neural networks to analyze and infer from complex data structures.

Dimensionality Reduction - The process of reducing the number of random variables under consideration, by obtaining a set of principal variables, crucial for simplifying data analytics models without significant loss of information.

Evaluation Metric - Measures used to assess the performance of a data analytics model or algorithm, helping analysts understand the effectiveness of their analytical models.

Feature Engineering - The process of using domain knowledge to extract features from raw data, a crucial step in improving the performance of data analytics algorithms.

Feature Learning - An aspect of some machine learning techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data.

Image Recognition - The ability of software to identify objects, people, places, and actions in images.

Label - In supervised learning, a label is the answer or outcome that the model is designed to predict, based on the input data.

Recommendation Engine - Systems that suggest products, services, information to users based on analysis of data.

Regression - A data analytics technique that estimates the relationships among variables, often used for prediction and forecasting.

Reinforcement Learning (RL) - An area of machine learning concerned with how software agents ought to take actions in an environment to maximize some notion of cumulative reward.

Structured Data - Data that is organized in a predefined manner, typically in databases, making it easily searchable and understandable by data analytics algorithms.

Unlabeled Data - Data that does not have explicit labels or annotations, often used in unsupervised learning tasks within data analytics to find hidden patterns or intrinsic structures.

Unstructured Data - Data that is not organized in a predefined way, common in text, images, and videos, which poses unique challenges and opportunities in data analytics.

Unsupervised Learning - Learning patterns from untagged data, without any given outcomes or answers.