Data Visualization and Analysis Concepts

These flashcards cover essential vocabulary and concepts related to data visualization and analysis for effective exam preparation.

Paired data

Sample size n with the observations formatted in pairs, allowing for comparison between two related datasets.

Correlation Coefficient (r)

A numerical measure that indicates the strength and direction of a linear relationship between two variables.

Regression Analysis

A statistical process for estimating the relationships among variables.

Simple Linear Regression

A method to model the relationship between a single predictor variable and a response variable.

Deterministic Regression Model

A regression model that does not account for error terms.

Probabilistic Regression Model

A regression model that incorporates error terms.

Extrapolation

The process of estimating unknown values by extending known values.

Residual

The difference between the observed value and the predicted value in a regression model.

Sum of Square Fit

A measure of how well a statistical model explains the variation in the data.

Cognitive Load

The mental effort required to process and understand information from a data visualization.

Preattentive Attributes

Visual properties that are processed effortlessly and automatically.

Color Psychology

The study of how colors influence human behavior and emotions.

Complementary Colors

Colors that are opposite each other on the color wheel, creating contrast.

Analogous Colors

Colors that are next to each other on the color wheel, creating harmony.

Gestalt Principles

Principles explaining how people perceive visual elements as unified wholes.

Similarity (Gestalt Principle)

The principle where objects with similar characteristics are perceived as belonging to the same group.

Proximity (Gestalt Principle)

The principle whereby objects physically close to each other are perceived as a group.

Enclosure (Gestalt Principle)

The principle that suggests objects enclosed together are perceived as a single group.

Connection (Gestalt Principle)

The principle that connected objects are seen as related or part of the same group.

Frequency Distribution

A summary of how often each category occurs within a dataset.

Bubble Chart

A data visualization that uses circles of varying sizes to represent three quantitative variables.

Heat Map

A graphical representation of data where values are represented by colors.

Natural Language Processing (NLP)

A field of artificial intelligence that focuses on the interaction between computers and human language.

Tokenization

The process of breaking down text into individual words or phrases.

Term Frequency (TF)

A measure of how often a term appears in a document relative to the total number of terms.

Inverse Document Frequency (IDF)

A measure that reflects how important a term is within the entire document set.

TF-IDF

A statistical measure that evaluates the importance of a word in a document relative to a collection of documents.

Sentiment Analysis

The process of determining the emotional tone behind a series of words.

Trend Line

The positive slope indicates there is a positive association between percentage the more tightly the points cluster around, strong relationship

Sum of Squares Due to Error

SSE

Total Sum of Squares

SST

Sum of Squares Due to Regression

SSR

Coefficient of Determination

A statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model.

Multiple Regression

regression analysis with two or more independent variables or with at least one nonlinear predictor

Multiple Regression Equation

An equation that models the relationship between multiple independent variables and a dependent variable, typically expressed in the form Y = b0 + b1X1 + b2X2 + … + bnXn + e.

Adjusted R²

to avoid adding extra variables that do not really belong, this value is typically listed in regression outputs

Data Visualization

the graphical representation of information and data, using visual elements like charts, graphs, and maps to communicate complex ideas.

Color

Features that can be processed by iconic memory and is the property of an object that results from the way the object reflects or emits light

Hues

Base of color

RYB

Traditional artist, adding model, Red, Yellow, Blue

CMY

Computers, Subtracting Model, cyan, yellow, magenta

Unnecessary use of Color

that can distract from the main message of a visualization.

Excessive use of color

that overwhelms the viewer and leads to confusion instead of clarity in a visualization.

Insufficient contract

between colors that makes elements difficult to distinguish or interpret in visualizations.

Inconsistency across related charts

can confuse viewers by failing to maintain a coherent design or color scheme, hindering effective comparison.

Orientation

positioning of an object within a data visualization

Size

amount of space an object occupies in a visualization, struggle to estimate relative size differences

Shape

form of objects used in data visualization to distinguish different groups

Length

the distance of a line or bar/column

Width

the thickness of a line or bar/column

Spatial Positioning

Pre attentive attribute of this focuses on the location of an object within some defined spaces

Frequency distribution

Bar chart

One continuous (numerical) variable

Histogram

Two categorial variables

contingency table/stacked column chart

Two continuous (numerical) variables

Scatter plot

Three continuous (numerical) variables

Bubble Plot

Timeseries

Line chart

Matrix Array

Heat Map

Geographic Map

a chart that shows characteristics and the arrangement of the geography of our physical reality

Data Dashboards

visual interfaces that display key metrics and trends, allowing users to analyze data from multiple sources.

Corpus

The entire body of text material to be analyzed (collection of documents)

Documents

the container of tokens chosen by the analyst

Text Analytics

Broader concept that includes information retrieval, where text mining primary focuses on discovering new and useful knowledge from the textual data sources

Text Mining

Knowledge in discovery in textual data

Information Extraction

identify key phrases and relationships with text by looking for predefined objects & sequences in text by way of pattern matching

Topic Tracking

Based on user profiles & documents that a user views, text mining can predict other documents of interest to the user

Summarization

Summary of documents to save time on the part of the reader

Clustering

letting themes emerge organically

Question Answering

finding the best answer to a given question through knowledge driven pattern machining

StopWord Removal

pare down the data removing words that don’t add any numerical value

Stemming

Process of removing prefixes, or suffixes - chop the word with letters in common

Lemmatization

Reducing the word to its lemma (dictionary entry) form

Term Document Matric TDM

bag of words technique counts the occurrence of words in a document while ignoring the order or the grammar of words

Binary Approach

the cells of the matrix are either populated with one (if token presented in document) or a zero (token not present)

Term Frequency Approach

Cells of matrix reflect the word count (frequency) in the document instead of just a zero or a one

Sparse Entry

A situation in a matrix where most of the entries are zero, indicating that only a small number of token occurrences are present compared to the total number of possible tokens.

TFIDF

value is specific to a single document whereas IDF entire corpus

Text Exploration

consists of techniques used to look for patterns or find relationships

Frequency Bar Chart

Consist of the x-axis representing terms and the y-axis representing the frequency of a particular term occurring

Word Cloud

is a visual representation of text data where the size of each word indicates its frequency or importance in the given text.

Text Modeling

Preprocessed data is used to build models

Classification

Most common knowledge discovery topic in analyzing complex data sources

Clustering

Unsupervised process where objects are classified into “natural groups” - problem is grouped into unlabeled collection of objects into meaningful clusters

Topic Modeling

Enables the analyst to discover hidden thematic structures in the text

Latent Dirichlet Allocation (LDA)

Goal is to maximize the separation between the estimated topics and minimize the variance within each projected topic

Sentiment Polarity

Classification of text as positive, negative, or neutral based on the emotional tone.