Dr. Shaun V. Ault; Dr. Soohyun Nam Liao; Larry Musolino

Symbols

3D visualization 9.5 Multivariate and Network Data Visualization Using Python

A

A prime 3.4 Probability Theory

accuracy 6.1 What Is Machine Learning?

Accurate representation 8.3 Ethics in Visualization and Reporting

ACF (autocorrelation function) 5.3 Time Series Forecasting Methods

Actionable advice 10.3 Effective Executive Summaries

Activation 7.1 Introduction to Neural Networks

activation function 7.1 Introduction to Neural Networks

adaptive learning 1.2 Data Science in Practice

additive decomposition 5.2 Components of Time Series Analysis

Advanced NLP models 7.5 Natural Language Processing

advertising 4.3 Correlation and Linear Regression Analysis


  AI Fairness 360

7.5 Natural Language Processing

alt text 10.1 Writing an Informative Report

alternative hypothesis 4.2 Hypothesis Testing

Amadeus Code 7.5 Natural Language Processing

Amazon 1.2 Data Science in Practice

Analysis of variance (ANOVA) 4.4 Analysis of Variance (ANOVA)

Anonymization 8.1 Ethics in Data Collection

Anonymous data 8.1 Ethics in Data Collection

application programming interface (API) 2.3 Web Scraping and Social Media Data Collection

apply.map 10.2 Validating Your Model

arithmetic mean 3.1 Measures of Center

Artificial intelligence (AI) Introduction

Association of Data Scientists 8.1 Ethics in Data Collection

Association of Data Scientists (ADaSci) 1.4 Using Technology for Data Science

assumption 10.2 Validating Your Model

at least one occurrence 3.4 Probability Theory

attributes 1.3 Data and Datasets

audience 10.1 Writing an Informative Report

Augmented Dickey-Fuller (ADF) test 5.3 Time Series Forecasting Methods

Autocorrelation 5.3 Time Series Forecasting Methods

autonomy 8.1 Ethics in Data Collection

Autoregressive (AR) model 5.3 Time Series Forecasting Methods

autoregressive integrated moving average (ARIMA) model 5.1 Introduction to Time Series Analysis

ax.scatter() 9.5 Multivariate and Network Data Visualization Using Python

B

Backpropagation 7.2 Backpropagation

bar

9.1 Encoding Univariate Data, 9.3 Graphing Probability Distributions

Bar graphs 9.1 Encoding Univariate Data

Bayes’ Theorem 3.4 Probability Theory

BCNF (Boyce-Codd Normal Form) 2.4 Data Cleaning and Preprocessing

Beautiful Soup 2.3 Web Scraping and Social Media Data Collection

Bernoulli trials 3.5 Discrete and Continuous Probability Distributions

best-fit linear equation 4.3 Correlation and Linear Regression Analysis

Bias 6.1 What Is Machine Learning?, 7.1 Introduction to Neural Networks, 8.2 Ethics in Data Analysis and Modeling

big data 2.5 Handling Large Datasets, 6.5 Other Machine Learning Techniques, 9.5 Multivariate and Network Data Visualization Using Python

bimodal 3.1 Measures of Center

binary (binomial) classification 6.2 Classification Using Machine Learning

Binary cross entropy loss 7.2 Backpropagation

binom() function 3.5 Discrete and Continuous Probability Distributions

binomial distribution 3.5 Discrete and Continuous Probability Distributions, 4.1 Statistical Inference and Confidence Intervals, 9.3 Graphing Probability Distributions

binomial experiment 3.5 Discrete and Continuous Probability Distributions

bins 9.1 Encoding Univariate Data

bivariate data 4.3 Correlation and Linear Regression Analysis, 9.1 Encoding Univariate Data

bootstrap aggregating (bagging) 6.5 Other Machine Learning Techniques

bootstrap samples 10.2 Validating Your Model

bootstrapping 4.1 Statistical Inference and Confidence Intervals, 6.3 Machine Learning in Regression Analysis

box-and-whisker plot 9.1 Encoding Univariate Data

boxplot 9.1 Encoding Univariate Data, 9.1 Encoding Univariate Data

Bureau of Labor Statistics (BLS) 1.3 Data and Datasets, 1 Group Project

business 1.2 Data Science in Practice

C

Careers in Data Science 1.2 Data Science in Practice

Categorical data 1.3 Data and Datasets

cell 1.5 Data Science with Python

central limit theorem 4.1 Statistical Inference and Confidence Intervals

central tendency 3.1 Measures of Center

centroid 6.2 Classification Using Machine Learning

ChatGPT 7.5 Natural Language Processing

choropleth graph 9.4 Geospatial and Heatmap Data Visualization Using Python

cirrhosis.csv 6.5 Other Machine Learning Techniques, 6 Group Project

Closed-ended questions 2.2 Survey Design and Implementation

Cloud computing 2.5 Handling Large Datasets

Cloud storage 2.5 Handling Large Datasets

cluster 6.2 Classification Using Machine Learning

cluster sampling 2.2 Survey Design and Implementation

codebook 10.1 Writing an Informative Report

coefficient of variation (CV) 3.2 Measures of Variation

Colab 1.5 Data Science with Python

CollegeCompletionData.csv 6.2 Classification Using Machine Learning, 6.3 Machine Learning in Regression Analysis, 6 Group Project

comma-separated values (CSV) 1.3 Data and Datasets

complement of an event 3.4 Probability Theory

CoNaLa 7.5 Natural Language Processing

conditional probability 3.4 Probability Theory

confidence interval 4.1 Statistical Inference and Confidence Intervals

confidence intervals 3.5 Discrete and Continuous Probability Distributions

confidence level 4.1 Statistical Inference and Confidence Intervals

Confidentiality 8.1 Ethics in Data Collection

confusion matrix 6.2 Classification Using Machine Learning

connecting weight 7.2 Backpropagation

constraints 10.2 Validating Your Model

consumer analysis Introduction

continuous 9.2 Encoding Data That Change Over Time

continuous data 1.3 Data and Datasets

Continuous probability distributions 3.5 Discrete and Continuous Probability Distributions

Continuous random variable 3.5 Discrete and Continuous Probability Distributions

Convenience sampling 2.2 Survey Design and Implementation

Convolutional layers 7.4 Convolutional Neural Networks

Convolutional neural networks (CNNs) 7.4 Convolutional Neural Networks

Cookies 8.1 Ethics in Data Collection

copyright 8.1 Ethics in Data Collection

Correctional Offender Management Profiling for Alternative Sanctions 8.2 Ethics in Data Analysis and Modeling

correlation Introduction

correlation analysis Introduction, 4.3 Correlation and Linear Regression Analysis, 9.5 Multivariate and Network Data Visualization Using Python

correlation coefficient 4.3 Correlation and Linear Regression Analysis

correlation heatmap 9.5 Multivariate and Network Data Visualization Using Python

critical value 4.1 Statistical Inference and Confidence Intervals

Cross-validation 8.2 Ethics in Data Analysis and Modeling, 10.2 Validating Your Model

cryptocurrencies 8.1 Ethics in Data Collection

CSV library 2.3 Web Scraping and Social Media Data Collection

customer surveys 3.1 Measures of Center

cusum() 9.1 Encoding Univariate Data

Cyclic component 5.2 Components of Time Series Analysis

D

DALL-E and Craiyon 7.5 Natural Language Processing

dashboards 9.5 Multivariate and Network Data Visualization Using Python

data 1.3 Data and Datasets

Data aggregation 2.4 Data Cleaning and Preprocessing

data analysis 1.1 What Is Data Science?

data breach 8.1 Ethics in Data Collection

Data chunking 2.5 Handling Large Datasets

data cleaning 6.5 Other Machine Learning Techniques

Data collection 1.1 What Is Data Science?, 2.1 Overview of Data Collection Methods, 8.1 Ethics in Data Collection

Data collection and preparation Introduction

Data compression 2.5 Handling Large Datasets

data dictionary 2.3 Web Scraping and Social Media Data Collection

Data discretization 2.4 Data Cleaning and Preprocessing

data governance protocols 8.1 Ethics in Data Collection

Data indexing 2.5 Handling Large Datasets

Data integration 2.4 Data Cleaning and Preprocessing

data irregularities 2.4 Data Cleaning and Preprocessing

data management 2.5 Handling Large Datasets

Data mining 6.5 Other Machine Learning Techniques

data preparation 1.1 What Is Data Science?

Data privacy Introduction

Data processing 2.3 Web Scraping and Social Media Data Collection

Data reporting 1.1 What Is Data Science?

data retention 8.1 Ethics in Data Collection

Data science 1.1 What Is Data Science?

Data Science Association 8.1 Ethics in Data Collection

Data Science Association (DSA) 1.4 Using Technology for Data Science

data science cycle 1.1 What Is Data Science?

data security 8.1 Ethics in Data Collection, 8.1 Ethics in Data Collection

Data sharing 8.1 Ethics in Data Collection

Data source attribution 8.3 Ethics in Visualization and Reporting

data sovereignty 8.1 Ethics in Data Collection

Data standardization 2.4 Data Cleaning and Preprocessing

Data transformation 2.4 Data Cleaning and Preprocessing, 2.4 Data Cleaning and Preprocessing

Data validation 2.4 Data Cleaning and Preprocessing, 2.4 Data Cleaning and Preprocessing, 8.2 Ethics in Data Analysis and Modeling

Data visualization 1.1 What Is Data Science?, Introduction

data volumes Introduction

data warehouse 2.5 Handling Large Datasets

data warehousing 1.1 What Is Data Science?

Data.gov 1.3 Data and Datasets, 1 Group Project

Database management 2.5 Handling Large Datasets

DataFrame 1.5 Data Science with Python, 2.3 Web Scraping and Social Media Data Collection

DataFrame.describe() 3.2 Measures of Variation

dataset 1.3 Data and Datasets


  DBSCAN

6.2 Classification Using Machine Learning

DBScan algorithm 6.2 Classification Using Machine Learning

DBScanExample.csv 6.2 Classification Using Machine Learning, 6 Group Project

decision tree 6.4 Decision Trees

decision-making Introduction

Deep learning 7.3 Introduction to Deep Learning

Deepfake 7.5 Natural Language Processing

degree 5.3 Time Series Forecasting Methods

density-based clustering algorithm 6.2 Classification Using Machine Learning

Dependent events 3.4 Probability Theory

dependent samples 4.2 Hypothesis Testing

dependent variable 4.3 Correlation and Linear Regression Analysis

depth 6.4 Decision Trees, 7.3 Introduction to Deep Learning

Depth-limiting pruning 6.4 Decision Trees

Descript 7.5 Natural Language Processing

descriptive statistics Introduction

Detrending 5.3 Time Series Forecasting Methods

df.info() 10.2 Validating Your Model

differencing 5.3 Time Series Forecasting Methods, 5.3 Time Series Forecasting Methods

digital divide 8.3 Ethics in Visualization and Reporting

dimension 7.1 Introduction to Neural Networks

discrete 9.2 Encoding Data That Change Over Time

discrete data 1.3 Data and Datasets

Discrete probability distributions 3.5 Discrete and Continuous Probability Distributions

Discrete random variable 3.5 Discrete and Continuous Probability Distributions, 9.3 Graphing Probability Distributions

dynamic backpropagation 7.2 Backpropagation

E

Education 1.2 Data Science in Practice, 7.5 Natural Language Processing

ELIZA 7.5 Natural Language Processing

empirical probability 3.4 Probability Theory

empirical rule 3.5 Discrete and Continuous Probability Distributions

encryption 8.1 Ethics in Data Collection

energy consumption 1.2 Data Science in Practice

engineering 1.2 Data Science in Practice

engineering analysis Introduction

Entropy 6.4 Decision Trees

epoch 7.2 Backpropagation

Equifax 8.1 Ethics in Data Collection

error 5.1 Introduction to Time Series Analysis

error-based (reduced-error) pruning 6.4 Decision Trees

ethical data collection 8.1 Ethics in Data Collection

Ethics in data science Introduction

event 3.4 Probability Theory

Excel 1.4 Using Technology for Data Science, Introduction, 3.5 Discrete and Continuous Probability Distributions, 3.5 Discrete and Continuous Probability Distributions, 4.1 Statistical Inference and Confidence Intervals, 5.1 Introduction to Time Series Analysis, 5.1 Introduction to Time Series Analysis, 5.2 Components of Time Series Analysis, 5.3 Time Series Forecasting Methods, 7.5 Natural Language Processing, 9.1 Encoding Univariate Data

executive (or data) dashboard 10.1 Writing an Informative Report

executive summary 10.3 Effective Executive Summaries

Executives 10.1 Writing an Informative Report

experimental research 2.1 Overview of Data Collection Methods

Experts 10.1 Writing an Informative Report

Explainable AI (XAI) 8.2 Ethics in Data Analysis and Modeling

exploding gradient problem 7.2 Backpropagation

expon 3.5 Discrete and Continuous Probability Distributions

exponential moving average (EMA) 5.3 Time Series Forecasting Methods

Exponential transformations 2.4 Data Cleaning and Preprocessing

Extensible Markup Language (XML) 1.3 Data and Datasets

F

f_oneway() 4.4 Analysis of Variance (ANOVA)

F-distribution 4.4 Analysis of Variance (ANOVA)

F1 Score 6.1 What Is Machine Learning?

Facial recognition 6.5 Other Machine Learning Techniques


  Fairlearn

7.5 Natural Language Processing

Fairness 8.2 Ethics in Data Analysis and Modeling

false positive 3.4 Probability Theory

Family Educational Rights and Privacy Act (FERPA) 8.1 Ethics in Data Collection

feature maps 7.4 Convolutional Neural Networks, 7.4 Convolutional Neural Networks

feedback loops 7.2 Backpropagation

fig.add.subplot() 9.5 Multivariate and Network Data Visualization Using Python

film industry 7.5 Natural Language Processing

five-number summary 9.1 Encoding Univariate Data

flat forecasting method 5.1 Introduction to Time Series Analysis

folds 10.2 Validating Your Model

forecasting 5.1 Introduction to Time Series Analysis

forecasting models 4.1 Statistical Inference and Confidence Intervals

frequency 3.1 Measures of Center

frequency distribution 3.1 Measures of Center

Fully connected layers 7.4 Convolutional Neural Networks

FungusLocations.csv 6.2 Classification Using Machine Learning, 6 Group Project

G

Gaussian naïve Bayes 6.5 Other Machine Learning Techniques

Gemini 7.5 Natural Language Processing

Generative art 7.5 Natural Language Processing

Geospatial data 9.4 Geospatial and Heatmap Data Visualization Using Python

Geospatial Information System (GIS) 9.4 Geospatial and Heatmap Data Visualization Using Python

Gini index 6.4 Decision Trees

GitHub 8.1 Ethics in Data Collection

GitHub Copilot 7.5 Natural Language Processing

Google 7.5 Natural Language Processing

Google Colab 7.5 Natural Language Processing, 8.1 Ethics in Data Collection

Google Colaboratory (Colab) 1.5 Data Science with Python

Google Sheets 1.4 Using Technology for Data Science

gradient descent 7.2 Backpropagation

graphical displays Introduction

Grid heatmaps 9.4 Geospatial and Heatmap Data Visualization Using Python

H

hallucinations 7.5 Natural Language Processing

hashing 8.2 Ethics in Data Analysis and Modeling

Health Insurance Portability and Accountability Act (HIPAA) 8.1 Ethics in Data Collection

heatmap 6.2 Classification Using Machine Learning, 9.5 Multivariate and Network Data Visualization Using Python

hidden layers 7.1 Introduction to Neural Networks

Hinge loss 7.2 Backpropagation

hist() 9.1 Encoding Univariate Data

histogram 9.1 Encoding Univariate Data


  HolisticAI

7.5 Natural Language Processing

Huffman coding 2.5 Handling Large Datasets

Hyperbolic tangent (tanh) function 7.1 Introduction to Neural Networks

hyperparameter tuning 10.2 Validating Your Model

hypothesis testing 3.5 Discrete and Continuous Probability Distributions, 4.2 Hypothesis Testing

I

imbalanced data 7.3 Introduction to Deep Learning

Implicit bias 10.2 Validating Your Model

imputation 2.4 Data Cleaning and Preprocessing

inconsistency 2.4 Data Cleaning and Preprocessing

Independent events 3.4 Probability Theory

independent samples 4.2 Hypothesis Testing

independent variable 4.3 Correlation and Linear Regression Analysis

indexing 2.3 Web Scraping and Social Media Data Collection

Inferential statistics Introduction

information 1.3 Data and Datasets

information gain 6.4 Decision Trees

Information theory 6.4 Decision Trees

Informed consent 8.1 Ethics in Data Collection

Initiative for Analytics and Data Science Standards 8.1 Ethics in Data Collection

Initiative for Analytics and Data Science Standards (IADSS) 1.4 Using Technology for Data Science

input layer 7.1 Introduction to Neural Networks

Integrative (I) component 5.3 Time Series Forecasting Methods

Intellectual property 8.1 Ethics in Data Collection

Internet of Things (IoT) 1.2 Data Science in Practice

interquartile range (IQR) 3.3 Measures of Position, 9.1 Encoding Univariate Data

items 1.3 Data and Datasets

J

jargon 10.1 Writing an Informative Report

JavaScript Object Notation (JSON) 1.3 Data and Datasets

Jukebox 7.5 Natural Language Processing

Jupyter Notebook 1.5 Data Science with Python

K

k-anonymization 8.2 Ethics in Data Analysis and Modeling

k-fold cross-validation 10.2 Validating Your Model

k-means clustering algorithm 6.2 Classification Using Machine Learning

Kaggle 1.3 Data and Datasets, 1 Group Project, 8.1 Ethics in Data Collection

Kapwing: 7.5 Natural Language Processing

key performance indicators (KPIs) 10.1 Writing an Informative Report

L

Labeled data 6.1 What Is Machine Learning?

lag 5.3 Time Series Forecasting Methods

large language model 7.5 Natural Language Processing

layered approach 10.1 Writing an Informative Report

Leaf-limiting pruning 6.4 Decision Trees

Leaky ReLU function 7.1 Introduction to Neural Networks

learning management systems 1.2 Data Science in Practice

least squares method 4.3 Correlation and Linear Regression Analysis

Leave-one-out cross-validation (LOOCV) 10.2 Validating Your Model

level 5.2 Components of Time Series Analysis

level of significance 4.2 Hypothesis Testing

likelihood 6.2 Classification Using Machine Learning

line chart 9.1 Encoding Univariate Data

linear correlation 4.3 Correlation and Linear Regression Analysis

linear regression Introduction

linregress() 4.3 Correlation and Linear Regression Analysis, 4.3 Correlation and Linear Regression Analysis

log transformation 2.4 Data Cleaning and Preprocessing

logistic regression 6.2 Classification Using Machine Learning


  LogisticRegression

6.2 Classification Using Machine Learning

logit function 6.2 Classification Using Machine Learning

long short-term memory (LSTM) network 7.2 Backpropagation

loss or cost function 7.2 Backpropagation

Lossless compression 2.5 Handling Large Datasets

Lossy compression 2.5 Handling Large Datasets

lower bound 3.3 Measures of Position

Lyria model 7.5 Natural Language Processing

M

machine learning (ML) model 6.1 What Is Machine Learning?

machine learning life cycle 6.1 What Is Machine Learning?

Magenta Project 7.5 Natural Language Processing

map

10.2 Validating Your Model

MAR (missing at random) data 2.4 Data Cleaning and Preprocessing

margin 7.3 Introduction to Deep Learning

margin of error 4.1 Statistical Inference and Confidence Intervals

margin of error formula 4.1 Statistical Inference and Confidence Intervals

matched pairs 4.2 Hypothesis Testing

Matplotlib 1.4 Using Technology for Data Science, 7.5 Natural Language Processing, 9.1 Encoding Univariate Data, 9.1 Encoding Univariate Data, 9.5 Multivariate and Network Data Visualization Using Python

matplotlib.pyplot 5.1 Introduction to Time Series Analysis

MCAR (missing completely at random) data 2.4 Data Cleaning and Preprocessing

mean 3.1 Measures of Center

Mean absolute error (MAE) 5.4 Forecast Evaluation Methods, 6.1 What Is Machine Learning?

Mean absolute percentage error (MAPE) 5.4 Forecast Evaluation Methods, 6.1 What Is Machine Learning?

mean percentage error (MPE) 10.2 Validating Your Model

Mean squared error (MSE) 6.1 What Is Machine Learning?

Measurement errors 2.2 Survey Design and Implementation

measures of error 5.4 Forecast Evaluation Methods

measures of fit 5.4 Forecast Evaluation Methods, 10.2 Validating Your Model

median 3.1 Measures of Center, 9.1 Encoding Univariate Data

memory cells 7.2 Backpropagation

metadata 2.5 Handling Large Datasets

method of least squares 4.3 Correlation and Linear Regression Analysis

Microsoft 7.5 Natural Language Processing

Minimum description length (MDL) pruning 6.4 Decision Trees

Missing data 2.4 Data Cleaning and Preprocessing

MNAR (missing not at random) data 2.4 Data Cleaning and Preprocessing

mode 3.1 Measures of Center

modeling 10.2 Validating Your Model

Monte Carlo simulation 10.2 Validating Your Model

MonthlyCoalConsumption.xlsx 5.2 Components of Time Series Analysis, 5.3 Time Series Forecasting Methods, 5.3 Time Series Forecasting Methods, 5.3 Time Series Forecasting Methods, 5 Group Project

mpl_toolkits.mplot3d() 9.5 Multivariate and Network Data Visualization Using Python


  mpl.toolkits.mplot3d

9.5 Multivariate and Network Data Visualization Using Python

Multi-way sensitivity analysis 10.2 Validating Your Model

multiclass (multinomial) classification 6.2 Classification Using Machine Learning

multilayer perceptron (MLP) 7.1 Introduction to Neural Networks

multiple regression 6.3 Machine Learning in Regression Analysis

multiplicative decomposition 5.2 Components of Time Series Analysis

N

naïve 5.1 Introduction to Time Series Analysis

naïve Bayes classification 6.5 Other Machine Learning Techniques

National Oceanic and Atmospheric Administration (NOAA) 1.3 Data and Datasets, 1 Group Project

natural language processing 7.5 Natural Language Processing

Natural Language Toolkit (NLTK) 2.3 Web Scraping and Social Media Data Collection

NCAA-2021-stats.csv 6.3 Machine Learning in Regression Analysis, 6.3 Machine Learning in Regression Analysis

Network analysis 2.3 Web Scraping and Social Media Data Collection

neural network 7.1 Introduction to Neural Networks

neurodiversity 10.1 Writing an Informative Report

neurons 7.1 Introduction to Neural Networks, 7.1 Introduction to Neural Networks

NLP models 7.5 Natural Language Processing

Noise 5.2 Components of Time Series Analysis, 5.2 Components of Time Series Analysis

Noisy data 2.4 Data Cleaning and Preprocessing

nominal data 1.3 Data and Datasets

non-response bias 2.2 Survey Design and Implementation

nonlinear 7.1 Introduction to Neural Networks

nonparametric method 4.1 Statistical Inference and Confidence Intervals

Nonspecialists 10.1 Writing an Informative Report

norm.cdf() 3.5 Discrete and Continuous Probability Distributions

norm() 3.5 Discrete and Continuous Probability Distributions

normal distribution 3.5 Discrete and Continuous Probability Distributions, 9.1 Encoding Univariate Data

normal form (NF) 2.4 Data Cleaning and Preprocessing

Normalization 2.4 Data Cleaning and Preprocessing

normalized 5.3 Time Series Forecasting Methods

NoSQL databases 2.5 Handling Large Datasets

np.array() function 4.3 Correlation and Linear Regression Analysis

null hypothesis 4.2 Hypothesis Testing

Numeric data 1.3 Data and Datasets

numpy 6.3 Machine Learning in Regression Analysis

NumPy 1.4 Using Technology for Data Science

O

object storage 2.5 Handling Large Datasets

Observational data 2.1 Overview of Data Collection Methods

odds 6.2 Classification Using Machine Learning

one-hot encoding 6.5 Other Machine Learning Techniques

one-way ANOVA 4.4 Analysis of Variance (ANOVA)

One-way sensitivity analysis 10.2 Validating Your Model

Open-ended questions 2.2 Survey Design and Implementation

OpenArt 7.5 Natural Language Processing

Order 5.3 Time Series Forecasting Methods

ordinal data 1.3 Data and Datasets

outcome 3.4 Probability Theory

outlier 2.4 Data Cleaning and Preprocessing

Outlier detection 8.2 Ethics in Data Analysis and Modeling

outliers Introduction, 3.1 Measures of Center, 9.1 Encoding Univariate Data

output layer 7.1 Introduction to Neural Networks

Overfitting 6.1 What Is Machine Learning?

P

p-value 4.2 Hypothesis Testing

P (A | B)

3.4 Probability Theory

paired samples 4.2 Hypothesis Testing

Panda 1.4 Using Technology for Data Science

pandas 2.3 Web Scraping and Social Media Data Collection, 2.3 Web Scraping and Social Media Data Collection, 9.1 Encoding Univariate Data, 10.2 Validating Your Model

pandas library 5.1 Introduction to Time Series Analysis

Parametric methods 4.1 Statistical Inference and Confidence Intervals

Pareto 9.1 Encoding Univariate Data

Pareto chart 9.1 Encoding Univariate Data

peak 5.3 Time Series Forecasting Methods

Pearsonr() 4.3 Correlation and Linear Regression Analysis, 4.3 Correlation and Linear Regression Analysis

Percentiles 3.3 Measures of Position

perceptron 7.1 Introduction to Neural Networks

period 5.2 Components of Time Series Analysis

personally identifiable information (PII) 8.1 Ethics in Data Collection, 8.1 Ethics in Data Collection

Pew Research Center 1.3 Data and Datasets, 1 Group Project


  plot

9.1 Encoding Univariate Data

plot.hist() 9.1 Encoding Univariate Data

Plotly 9.5 Multivariate and Network Data Visualization Using Python

plt.scatter() 4.3 Correlation and Linear Regression Analysis

point estimate 4.1 Statistical Inference and Confidence Intervals

point-in-time data 5.1 Introduction to Time Series Analysis

poisson 3.5 Discrete and Continuous Probability Distributions, 9.3 Graphing Probability Distributions

Poisson distribution 3.5 Discrete and Continuous Probability Distributions, 9.3 Graphing Probability Distributions

Pooling layers 7.4 Convolutional Neural Networks

Population data 3.1 Measures of Center

population mean 3.1 Measures of Center, 4.1 Statistical Inference and Confidence Intervals

population proportion 4.1 Statistical Inference and Confidence Intervals

population size 3.1 Measures of Center

population variance 3.2 Measures of Variation

potential for bias 7.5 Natural Language Processing

Power BI 8.1 Ethics in Data Collection

Precision 6.1 What Is Machine Learning?

Precision Medicine Initiative 1.2 Data Science in Practice

prediction interval 5.4 Forecast Evaluation Methods

predictions 4.3 Correlation and Linear Regression Analysis

predictive analytics 1.2 Data Science in Practice

principal component analysis (PCA) 6.1 What Is Machine Learning?

prior probability 6.5 Other Machine Learning Techniques

Probability 3.4 Probability Theory

Probability analysis Introduction

probability density function (PDF) 3.5 Discrete and Continuous Probability Distributions, 9.3 Graphing Probability Distributions, 9.3 Graphing Probability Distributions

Probability distribution 3.5 Discrete and Continuous Probability Distributions

probability experiment 3.4 Probability Theory

probability mass function (PMF) 3.5 Discrete and Continuous Probability Distributions

programming language 1.4 Using Technology for Data Science

proportion 4.1 Statistical Inference and Confidence Intervals

Pruning 6.4 Decision Trees

Pseudonymization 8.1 Ethics in Data Collection

Public Policy 1.2 Data Science in Practice

purposive sampling 2.2 Survey Design and Implementation

Python 1.4 Using Technology for Data Science, 3.5 Discrete and Continuous Probability Distributions, 4.1 Statistical Inference and Confidence Intervals, 5.1 Introduction to Time Series Analysis, 5.1 Introduction to Time Series Analysis, 5.3 Time Series Forecasting Methods, 5.4 Forecast Evaluation Methods, 9.1 Encoding Univariate Data

Q

qualitative 3.1 Measures of Center

Qualitative data 1.3 Data and Datasets, 8.1 Ethics in Data Collection, 9.2 Encoding Data That Change Over Time

quantitative 3.1 Measures of Center

Quantitative data 1.3 Data and Datasets, 8.1 Ethics in Data Collection, 9.2 Encoding Data That Change Over Time

quartiles 3.3 Measures of Position, 9.1 Encoding Univariate Data

Quota sampling 2.2 Survey Design and Implementation

R

R 1.4 Using Technology for Data Science, Introduction, 3.5 Discrete and Continuous Probability Distributions, 3.5 Discrete and Continuous Probability Distributions

Random Error 5.2 Components of Time Series Analysis

random forest 6.5 Other Machine Learning Techniques

Random variable 3.5 Discrete and Continuous Probability Distributions

Range 3.2 Measures of Variation

re

2.3 Web Scraping and Social Media Data Collection

read_html() 2.3 Web Scraping and Social Media Data Collection

Recall 6.1 What Is Machine Learning?

recommendation systems 1.2 Data Science in Practice

Rectified linear unit (ReLU) function 7.1 Introduction to Neural Networks

recurrent neural networks (RNNs) 7.2 Backpropagation

RedBlue.csv 6.4 Decision Trees

redundancy 2.4 Data Cleaning and Preprocessing

regex 2.3 Web Scraping and Social Media Data Collection

Regression analysis Introduction

regression models 10.2 Validating Your Model

regression tree 6.4 Decision Trees

Regular expressions 2.3 Web Scraping and Social Media Data Collection

regulatory compliance officer (RCO) 8.1 Ethics in Data Collection

Relational databases 2.5 Handling Large Datasets

Relative frequency probability 3.4 Probability Theory


  replace

10.2 Validating Your Model

reproducibility, 10.1 Writing an Informative Report

residual 4.3 Correlation and Linear Regression Analysis

residuals 5.2 Components of Time Series Analysis

responsible AI 7.5 Natural Language Processing

revenue 4.3 Correlation and Linear Regression Analysis

Root mean squared error (RMSE) 5.4 Forecast Evaluation Methods, 6.1 What Is Machine Learning?

round() function 3.5 Discrete and Continuous Probability Distributions

Russian tank problem 6.1 What Is Machine Learning?

S

S&P 500 4.3 Correlation and Linear Regression Analysis

sabermetrics 1.2 Data Science in Practice

Sample data 3.1 Measures of Center

sample mean 3.1 Measures of Center, 4.1 Statistical Inference and Confidence Intervals

sample proportion 4.1 Statistical Inference and Confidence Intervals, 4.2 Hypothesis Testing

sample size 3.1 Measures of Center

sample size requirement 4.1 Statistical Inference and Confidence Intervals

sample space 3.4 Probability Theory

sample statistic 4.1 Statistical Inference and Confidence Intervals

sample variance 3.2 Measures of Variation

Sampling 2.2 Survey Design and Implementation, 4.1 Statistical Inference and Confidence Intervals

sampling (or sample) distribution 4.1 Statistical Inference and Confidence Intervals

Sampling Bias 2.2 Survey Design and Implementation

Sampling error 2.2 Survey Design and Implementation

sampling frame 2.2 Survey Design and Implementation

sampling techniques 2.2 Survey Design and Implementation

sampling variability 2.2 Survey Design and Implementation

Sampling with replacement 4.1 Statistical Inference and Confidence Intervals

Sarbanes-Oxley Act [SOX] 8.1 Ethics in Data Collection

scale-dependent 5.4 Forecast Evaluation Methods

scaling 2.4 Data Cleaning and Preprocessing


  scatter

9.1 Encoding Univariate Data

scatterplot 9.5 Multivariate and Network Data Visualization Using Python

scatterplot, or scatter diagram 4.3 Correlation and Linear Regression Analysis

Scenario analysis 10.2 Validating Your Model

science 1.2 Data Science in Practice

scientific experiments 2.1 Overview of Data Collection Methods


  Scikit-Learn

7.5 Natural Language Processing


  scipy

library 4.4 Analysis of Variance (ANOVA)


  SciPy library

4.1 Statistical Inference and Confidence Intervals

scipy.stats 3.5 Discrete and Continuous Probability Distributions, 6.3 Machine Learning in Regression Analysis

scipy.stats library 4.1 Statistical Inference and Confidence Intervals, 4.1 Statistical Inference and Confidence Intervals, 4.3 Correlation and Linear Regression Analysis

Seaborn 1.4 Using Technology for Data Science, 6.2 Classification Using Machine Learning

Seasonal component 5.2 Components of Time Series Analysis

seasonal variations 5.2 Components of Time Series Analysis

Seasonal-Trend decomposition using LOESS (STL) 5.3 Time Series Forecasting Methods

seasonality 5.2 Components of Time Series Analysis, 5.2 Components of Time Series Analysis, 5.3 Time Series Forecasting Methods

self-selection bias 2.2 Survey Design and Implementation

semantic segmentation 7.4 Convolutional Neural Networks

Sensitivity analysis 10.2 Validating Your Model

sigmoid function 6.2 Classification Using Machine Learning, 7.1 Introduction to Neural Networks

silhouette score 6.2 Classification Using Machine Learning

Simon Says 7.5 Natural Language Processing

simple moving average (SMA) 5.3 Time Series Forecasting Methods

Simple random selection 2.2 Survey Design and Implementation


  sklearn

6.4 Decision Trees, 6.5 Other Machine Learning Techniques, 10.2 Validating Your Model

sklearn.cluster 6.2 Classification Using Machine Learning

sklearn.datasets 7.1 Introduction to Neural Networks


  sklearn.ensemble

6.5 Other Machine Learning Techniques

sklearn.linear_model 6.2 Classification Using Machine Learning

Slicing a string 2.3 Web Scraping and Social Media Data Collection

smart city 1.2 Data Science in Practice

Snowball sampling 2.2 Survey Design and Implementation

sns.heatmap() 9.4 Geospatial and Heatmap Data Visualization Using Python

Social listening 2.3 Web Scraping and Social Media Data Collection

Social media data collection 2.3 Web Scraping and Social Media Data Collection

softmax 7.1 Introduction to Neural Networks

Softplus function 7.1 Introduction to Neural Networks

Sonar 7.5 Natural Language Processing

SP500.csv 5.1 Introduction to Time Series Analysis, 5 Group Project

SpaCy 2.3 Web Scraping and Social Media Data Collection

Sparse categorical cross entropy 7.2 Backpropagation

Spatial heatmaps 9.4 Geospatial and Heatmap Data Visualization Using Python

speech recognition 7.5 Natural Language Processing

Splitting a string 2.3 Web Scraping and Social Media Data Collection

sports analytics 1.2 Data Science in Practice

Spreadsheet programs 1.4 Using Technology for Data Science

square root transformation 2.4 Data Cleaning and Preprocessing

standard deviation 3.2 Measures of Variation

standard error of the mean 4.1 Statistical Inference and Confidence Intervals

standard normal distribution 3.5 Discrete and Continuous Probability Distributions

standardized test statistic 4.2 Hypothesis Testing

standardizing data 2.4 Data Cleaning and Preprocessing

static backpropagation 7.2 Backpropagation

Statista 1.3 Data and Datasets, 1 Group Project

statistical analysis 2.1 Overview of Data Collection Methods, Introduction

statistical validation 10.2 Validating Your Model

statistics 6.3 Machine Learning in Regression Analysis

statsmodels 5.3 Time Series Forecasting Methods

statsmodels.tsa.arima.model 5.3 Time Series Forecasting Methods, 5.4 Forecast Evaluation Methods

Step function 7.1 Introduction to Neural Networks

Stratified sampling 2.2 Survey Design and Implementation

structured dataset 1.3 Data and Datasets

sum of squares 3.2 Measures of Variation

Supervised learning 6.1 What Is Machine Learning?

supervised learning cycle 6.1 What Is Machine Learning?

survey 2.1 Overview of Data Collection Methods

Symmetric mean absolute percentage error (sMAPE) 5.4 Forecast Evaluation Methods

Systematic sampling 2.2 Survey Design and Implementation

T

t-distribution 4.1 Statistical Inference and Confidence Intervals

t.ppf() 4.1 Statistical Inference and Confidence Intervals

Tableau 8.1 Ethics in Data Collection

TabNine 7.5 Natural Language Processing

Technicians 10.1 Writing an Informative Report

temps.csv 6.5 Other Machine Learning Techniques, 6.5 Other Machine Learning Techniques

tensor 7.2 Backpropagation

terms 5.1 Introduction to Time Series Analysis

test statistic 4.2 Hypothesis Testing


  test_size=0.25

7.1 Introduction to Neural Networks

testing set (or data) 6.1 What Is Machine Learning?

Text preprocessing 2.4 Data Cleaning and Preprocessing

text-to-speech (TTS) 7.5 Natural Language Processing

TextBlob 2.3 Web Scraping and Social Media Data Collection

Theoretical probability 3.4 Probability Theory

time series 5.1 Introduction to Time Series Analysis

time series model 5.1 Introduction to Time Series Analysis

to_datatime() 5.1 Introduction to Time Series Analysis

to_numeric() 2.3 Web Scraping and Social Media Data Collection

Tokenizing 2.3 Web Scraping and Social Media Data Collection

topological data analysis (TDA) 6.1 What Is Machine Learning?

training set (or data) 6.1 What Is Machine Learning?

Transactional data 2.1 Overview of Data Collection Methods

Transformer models 7.5 Natural Language Processing

transparency 8.1 Ethics in Data Collection

Trend 5.2 Components of Time Series Analysis

trend curve 5.2 Components of Time Series Analysis

trend-cycle component 5.2 Components of Time Series Analysis

trendline 5.2 Components of Time Series Analysis

trimmed mean 3.1 Measures of Center, 3.1 Measures of Center

ttest_1samp() 4.2 Hypothesis Testing


  twinx

9.1 Encoding Univariate Data

Type I error 4.2 Hypothesis Testing

Type II error 4.2 Hypothesis Testing

U

unbiased estimator 4.1 Statistical Inference and Confidence Intervals

Underfitting 6.1 What Is Machine Learning?

Univariate data 9.1 Encoding Univariate Data

Universal design principles 8.3 Ethics in Visualization and Reporting

Unlabeled data 6.1 What Is Machine Learning?

unstructured data Introduction

unstructured dataset 1.3 Data and Datasets

Unsupervised learning 6.1 What Is Machine Learning?

upper bound 3.3 Measures of Position

V

Validation 10.2 Validating Your Model

vanishing gradient problem 7.2 Backpropagation

variable Introduction

variance 3.2 Measures of Variation, 6.1 What Is Machine Learning?

vector 7.1 Introduction to Neural Networks

version control system 10.1 Writing an Informative Report

volatility 5.1 Introduction to Time Series Analysis

Volunteer sampling 2.2 Survey Design and Implementation

VTK 9.5 Multivariate and Network Data Visualization Using Python

W

Walmart 1.2 Data Science in Practice

weak learners 6.5 Other Machine Learning Techniques

weather forecasting 1.2 Data Science in Practice

Web scraping 2.3 Web Scraping and Social Media Data Collection

weight 7.1 Introduction to Neural Networks

weighted moving average (WMA) 5.3 Time Series Forecasting Methods


  What-If Tool

7.5 Natural Language Processing

white noise 5.2 Components of Time Series Analysis

window 5.3 Time Series Forecasting Methods

World Health Organization (WHO) 1.3 Data and Datasets

X

XML tag 1.3 Data and Datasets

Z

z

-score 3.3 Measures of Position

Index