Introduction to Data Analytics – Vocabulary Flashcards

A set of vocabulary flashcards covering core concepts, terms and definitions from UCS551 Chapters 1 & 2 on Introduction to Data Analytics and Data Understanding.

Data

A set of values relating to qualitative or quantitative variables; becomes information when interpreted in context.

Data Analytics

The process of inspecting, cleansing, transforming and modeling data to discover useful information, support conclusions and aid decision-making.

Big Data

Data that is expensive to manage and hard to extract value from due to its large Volume, Velocity, Variety, and related characteristics.

Volume (Big Data ‘V’)

The sheer size or amount of data that defines it as “big.”

Velocity (Big Data ‘V’)

The speed at which data is generated, processed and accessed.

Variety (Big Data ‘V’)

The diversity of data sources, formats, qualities and structures.

Variability (Big Data ‘V’)

The way data constantly changes, requiring interpretation of shifting meanings.

Veracity (Big Data ‘V’)

The accuracy, reliability and quality of data gathered.

Visualization (Big Data ‘V’)

The presentation of data in visual form to support managerial decision-making.

Value (Big Data ‘V’)

The benefit an organization gains after investing effort in the other V’s of big data.

Structured Data

Information organized in a predefined model that is easily searchable, e.g., relational databases and spreadsheets.

Unstructured Data

Information lacking a predefined structure, making it time-consuming to search using traditional methods, e.g., emails, images, documents.

Semi-structured Data

Data that does not reside in a relational database but has some organizational properties, e.g., XML or JSON.

Streaming Data

Data that arrives continuously and must be processed in near real time.

Relational Data

Structured data stored in tables with rows and columns, often managed by SQL databases.

Vector

A one-dimensional array storing elements of the same type.

Array

A collection of elements identified by index or index tuple; can be one-dimensional or multidimensional.

Matrix

A two-dimensional array of numbers arranged in rows and columns.

Data Frame

A tabular data structure combining multiple vectors as columns where each column is homogeneous and rows represent observations.

List (Data Structure)

An ordered collection of elements that can be of different data types.

Factor (Data Structure)

A data type in statistical software used to handle categorical variables and their levels.

Nominal Level of Measurement

Data categorized without any intrinsic order (e.g., gender, race).

Ordinal Level of Measurement

Categorical data with a meaningful order but unequal intervals (e.g., satisfaction ratings).

Interval Level of Measurement

Numeric data with equal intervals but no true zero (e.g., temperature in °F).

Ratio Level of Measurement

Numeric data with equal intervals and a true zero, allowing ratios (e.g., weight, sales).

Univariate Data

A data set that consists of a single variable, often analyzed with a vector.

Multivariate Data

A data set containing multiple variables, commonly stored in a matrix or data frame.

Descriptive Analytics

Analytics that explain what has happened over a given period.

Diagnostic Analytics

Analytics that investigate why something happened, using diverse data inputs and hypotheses.

Predictive Analytics

Analytics that forecast what is likely to happen in the near future.

Prescriptive Analytics

Analytics that recommend actions based on predictive insights.

Customer Analytics

Use of analytics to model and understand customer behavior and loyalty.

Credit Risk Analytics

Analytical techniques applied to credit data to assess and manage risk.

Retail Analytics

Analytics used to forecast demand and optimize retail operations.

Marketing Analytics

Analytics that evaluate product, pricing, promotion and distribution strategies.

Business Analytics (Churn)

Analyzing business data to identify customers likely to stop using a service and inform retention strategies.

Data Science

A multidisciplinary field that extracts insights from data using mathematics, statistics, AI and computer engineering.

Data Analytics Process – Ask

Define the problem and formulate questions to guide analysis.

Data Analytics Process – Prepare

Collect, combine and store relevant data for analysis.

Data Analytics Process – Process

Clean and verify data to ensure quality and readiness.

Data Analytics Process – Analyze

Search for patterns, relationships and trends within the data.

Data Analytics Process – Share

Communicate findings to stakeholders using reports and visualizations.

Data Analytics Process – Act

Implement decisions and actions based on analytical insights.

Central Tendency

Measures that describe the center of a data set: mean, median, mode.

Dispersion

Measures that describe the spread of data: range, variance, standard deviation.

Data Exploration

Initial analysis phase aimed at understanding data distributions, key attributes, correlations, outliers and quality.