Tech Glossary: Data Analytics
Data Analytics
Big Data
Extremely large and complex datasets that require specialized tools and techniques for storage, processing, and analysis due to their volume, variety, and velocity.
Causation
The relationship between cause and effect, where a change in one variable directly influences a change in another. Establishing causation often requires careful experimental design.
Classification
A machine learning task where the goal is to assign a category or label to input data based on the patterns learned from training examples.
Cluster Analysis
A method used to group similar data points or objects into clusters, helping to identify patterns or segments within the data.
Correlation
A statistical measure that describes the degree to which two variables change together. Positive correlation indicates that as one variable increases, the other tends to increase, and vice versa for negative correlation.
Data Analytics
The process of examining, cleaning, transforming, and interpreting data to discover useful insights, patterns, and trends, often with the goal of making informed decisions.
Data Mining
The process of discovering hidden patterns, trends, or knowledge within large datasets using techniques from various disciplines, such as statistics, machine learning, and artificial intelligence.
Data Visualization
The graphical representation of data to make it more understandable and interpretable, aiding in the communication of insights to both technical and non-technical audiences.
Data Wrangling
The process of cleaning, transforming, and preparing raw data into a format suitable for analysis, often involving tasks like dealing with missing values and standardizing data types.
Descriptive Analytics
The initial phase of data analysis that focuses on summarizing historical data to provide a clear picture of what has happened in the past.
Diagnostic Analytics
A type of analytics that aims to determine why a particular event or outcome occurred by investigating the underlying causes and relationships within the data.
Dimensionality Reduction
Techniques that reduce the number of features in a dataset while preserving its essential characteristics, aiding in visualization and analysis.
Exploratory Data Analysis (EDA)
The process of visually and statistically exploring a dataset to identify patterns, relationships, and potential outliers, helping to inform further analysis.
Feature Engineering
The process of selecting, transforming, or creating relevant features (variables) from raw data to improve the performance of machine learning algorithms.
Machine Learning
A subset of artificial intelligence that involves developing algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed.
Natural Language Processing (NLP)
A field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language, allowing for the analysis of textual data.
Predictive Analytics
The use of historical data and statistical algorithms to forecast future events or trends. It involves making predictions based on patterns and relationships found in the data.
Prescriptive Analytics
A more advanced form of analytics that not only predicts future outcomes but also suggests actions or interventions to achieve desired outcomes. It combines predictive analytics with decision-making strategies.
Regression Analysis
A statistical technique used to model the relationship between one or more independent variables and a dependent variable, aiming to predict numerical outcomes.
Time Series Analysis
The study of data points collected at specific time intervals to identify patterns, trends, and seasonality, often used for forecasting future values.
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