Index

A

• A/B test
  • about / Experimental
• Adam Optimizer
  • about / Tensorflow and neural networks
• addition rule / The addition rule
• alternative hypothesis
  • about / Hypothesis tests
• anomaly detection / Basic structure
• ARIMA / Going beyond with this example
• arithmetic mean / Measures of center
  • about / Measures of center
• arithmetic symbols
  • about / Arithmetic symbols
  • summation / Summation
  • proportional / Proportional
  • dot product / Dot product

B

• back-propagation / Basic structure
• bar charts
  • about / Bar charts
• basic Python, example
  • about / Example of basic Python
  • single Tweet, parsing / Example – parsing a single tweet
• Bayes formula
  • about / Proportional
• Bayes theorem
  • about / Bayes theorem
  • examples / Bayes theorem
  • applications / More applications of Bayes theorem
  • titanic dataset / Example – Titanic
  • medical studies example / Example – medical studies
• bi-modal
  • about / Sampling distributions
• bias/variance tradeoff
  • extreme cases / Two extreme cases of bias/variance tradeoff
  • working, with error functions / How bias/variance play into error functions
• bias variance tradeoff
  • about / The bias variance tradeoff
  • error, due to bias / Error due to bias
  • error, due to variance / Error due to variance
• big data
  • about / Some more terminology
• binary classifier / A bit deeper
• binomial random variable
  • about / Binomial random variables
  • restaurant openings example / Binomial random variables
  • blood types example / Binomial random variables
• box plots
  • about / Box plots
  • creating / Box plots

C

• Cartesian graph / Graphs
• causation
  • versus correlation / Correlation versus causation
• central limit theorem
  • about / Sampling distributions
• centroid
  • about / K-means clustering
• chi-square goodness of fit test
  • about / Chi-square goodness of fit test
  • asuumptions / Assumptions of the chi-square goodness of fit test
  • example / Example of a chi-square test for goodness of fit
• chi-square test for association/independence
  • about / Chi-square test for association/independence
  • assumptions / Assumptions of the chi-square independence test
• classification
  • about / Classification
• classification tree
  • fitting / How does a computer fit a classification tree?
• cluster
  • about / K-means clustering
• clustering
  • about / Unsupervised learning
• coefficient of variation
  • about / Definition
  • employee salaries example / Example – employee salaries
• collectively exhaustive / Collectively exhaustive events
• collectively exhaustive events
  • about / Collectively exhaustive events
  • examples / Collectively exhaustive events
• communication
  • about / Why does communication matter?
• complementary events / Complementary events
• compound events
  • about / Compound events
  • example / Compound events
• conditional probability
  • about / Conditional probability
• confidence
  • about / Confidence intervals
• confidence intervals
  • about / Confidence intervals
• confounding factor / Random sampling
• confusion matrix / A bit deeper
• continuous data
  • about / Digging deeper
  • example / Digging deeper
• continuous random variable
  • about / Continuous random variables
• correlation
  • versus causation / Correlation versus causation
• correlation coefficients
  • about / The insightful part – correlations in data
• cross validation error
  • versus training error visualization / Visualizing training error versus cross-validation error
• CSV (comma separated value) / Example – world alcohol consumption data

D

• data
  • organized data / Basic terminology
  • unorganized data / Basic terminology
  • types / Flavors of data, Why look at these distinctions?
  • levels / The four levels of data
• data, obtaining
  • about / Obtaining data
  • observational / Observational
  • experimental / Experimental
• data exploration
  • about / Explore the data
  • basic questions / Basic questions for data exploration
  • yelp dataset / Dataset 1 – Yelp
  • titanic dataset / Dataset 2 – titanic
• data mining
  • about / Some more terminology
• data model
  • about / The math
• data points
  • about / Illustrative example – data points
• data preprocessing
  • example / Example of data preprocessing
  • word/phrase counts / Word/phrase counts
  • relative length of text / Relative length of text
  • topics, picking / Picking out topics
• data sampling
  • about / Sampling data
  • probability sampling / Probability sampling
  • random sampling / Random sampling
  • unequal probability sampling / Unequal probability sampling
• data science
  • about / What is data science?, Basic terminology, The data science Venn diagram, Introduction to data science
  • need for / Why data science?
  • Sigma Technologies example / Example – Sigma Technologies
  • steps / Overview of the five steps
  • interesting question, asking / Ask an interesting question
  • data, obtaining / Obtain the data
  • data, exploring / Explore the data
  • data, modeling / Model the data
  • results, communicating / Communicate and visualize the results
  • results, visualizing / Communicate and visualize the results
• data science, case studies
  • about / Data science case studies
  • government paper pushing automation / Case study – automating government paper pushing, Fire all humans, right?
  • marketing dollars / Case study – marketing dollars
  • job descriptions / Case study – what's in a job description?
• data science Venn diagram
  • about / The data science Venn diagram
  • math/statistics / The data science Venn diagram, The math
  • computer programming / The data science Venn diagram, Computer programming
  • domain knowledge / The data science Venn diagram, Domain knowledge
• decision trees
  • about / Decision trees
  • versus random forests / Comparing Random forests with decision trees
• Deep Neural Network Classifier (DNNClassifier)
  • about / Tensorflow and neural networks
• dimension reduction
  • about / Unsupervised learning
  • cons / Feature extraction and principal component analysis
• discrete data
  • about / Digging deeper
  • example / Digging deeper
• discrete random variables
  • about / Discrete random variables
  • types / Types of discrete random variables
  • binomial random variable / Binomial random variables
  • geometric random variable / Binomial random variables
  • Poisson random variable / Poisson random variable,
  • continuous random variable / Continuous random variables
• domain knowledge / Domain knowledge
• Domain Knowledge / The data science Venn diagram
• dot product
  • about / Dot product
• dummy variables
  • about / Dummy variables

E

• Empirical rule
  • about / The Empirical rule
  • example / The Empirical rule
• ensembling techniques / Ensembling techniques
• entity movement / Basic structure
• entropy
  • about / How does a computer build a regression tree?
• error functions
  • about / How bias/variance play into error functions
• Euler's number / Logistic regression
• event
  • about / Basic definitions
• exploration tips, for qualitative data
  • about / Exploration tips for qualitative data
  • nominal level columns / Nominal level columns
  • filtering / Filtering in Pandas
  • ordinal level columns / Ordinal level columns
• exploratory data analysis (EDA)
  • about / Some more terminology
• exponent
  • about / Logarithms/exponents
  • examples / Logarithms/exponents
• extra-marital affairs case study
  • about / Case study 2 – why do some people cheat on their spouses?
• extreme cases, bias/variance tradeoff
  • underfitting / Underfitting
  • overfitting / Overfitting

F

• false negative
  • about / Type I and type II errors
• false negatives / A bit deeper
• false positive
  • about / Type I and type II errors
• false positives / A bit deeper
• feature extraction / The Silhouette Coefficient, Feature extraction and principal component analysis
  • about / Feature extraction and principal component analysis
  • pros / Feature extraction and principal component analysis
• feature selection
  • about / Feature extraction and principal component analysis
• filtering
  • about / Filtering in Pandas
• Frequentist approach
  • about / Frequentist approach
  • marketing stats example / Frequentist approach
  • law of large numbers / The law of large numbers

G

• geometric random variable
  • about / Binomial random variables
  • weather example / Binomial random variables
• gini index
  • about / How does a computer build a regression tree?
• global score / The Silhouette Coefficient
• graphs
  • about / Graphs
  • Cartesian graph / Graphs
  • scatter plots / Scatter plots
  • line graphs / Line graphs
  • bar charts / Bar charts
  • histograms / Histograms
  • box plots / Box plots
• grid searching
  • about / Grid searching

H

• histograms
  • about / Histograms
  • plotting / Histograms
• hypothesis test
  • about / Hypothesis tests
  • conducting / Conducting a hypothesis test
  • one sample t-tests / One sample t-tests
  • type II error / Type I and type II errors
  • type I error / Type I and type II errors
• hypothesis test, for categorical values
  • about / Hypothesis test for categorical variables
  • chi-square goodness of fit test / Chi-square goodness of fit test
  • chi-square test for association/independence / Chi-square test for association/independence

I

• independent events
  • examples / Independence
• intersection / Set theory
• interval level, of data
  • about / The interval level
  • example / Example
  • mathematical operations / Mathematical operations allowed
  • measures of center / Measures of center
  • measures of variation / Measures of variation

J

• jaccard measure / Set theory

K

• k-fold cross validation
  • about / Case study 3 – using tensorflow
• K-means clustering
  • about / K-means clustering
  • example / Illustrative example – beer!
• K-Nearest Neighbors (KNN) algorithm
  • about / How bias/variance play into error functions
• K folds cross validation
  • about / K folds cross-validation
  • features / K folds cross-validation
• KPI (key performance indicator)
  • about / Verbal communication

L

• labeled data / Supervised learning
• levels, data
  • nominal / The nominal level
  • ordinal / The ordinal level
  • interval / The interval level
  • ratio / The ratio level
• likelihood
  • about / Naïve Bayes classification
• likert scale
  • about / Discrete random variables
• linear algebra
  • about / Dot product, Linear algebra
  • matrix multiplication / Matrix multiplication
• linear regression
  • about / Linear regression
  • predictors, adding / Adding more predictors
• line graphs
  • about / Line graphs
• logarithm
  • about / Logarithms/exponents
  • examples / Logarithms/exponents
• logistic regression
  • about / Logistic regression, The math of logistic regression
• log odds
  • about / Probability, odds, and log odds

M

• machine learning
  • about / The data science Venn diagram, Example – spawner-recruit models, Some more terminology, What is machine learning?
  • facial recognition example / What is machine learning?
  • limitations / Machine learning isn't perfect
  • working / How does machine learning work?
  • types / Types of machine learning
  • supervised learning / Supervised learning
  • unsupervised learning / Unsupervised learning
  • overview / Overview of the types of machine learning
• magnitude / Set theory
• margin of error
  • about / Confidence intervals
• Math & Statistics Knowledge base / The data science Venn diagram
• mathematics
  • about / Mathematics as a discipline
• matrices
  • multiplying / How to multiply matrices
• matrix
  • about / Vectors and matrices
• matrix multiplication
  • about / Matrix multiplication
• measures of center / Measures of center
• measures of relative standing
  • about / Measures of relative standing
  • correlations, in data / The insightful part – correlations in data
• measures of variation
  • about / Measures of variation, Measures of relative standing
• median / Measures of center, Measures of center
• model coefficients
  • about / Linear regression
• models
  • about / The math
• multilayer perceptrons (MLP) / Basic structure
• multiplication rule / The multiplication rule
• mutual exclusivity / Mutual exclusivity
• mutually exhaustive / Collectively exhaustive events

N

• Naïve Bayes classification
  • about / Naïve Bayes classification
• neural networks
  • about / Neural networks
  • basic structure / Basic structure
  • advantage / Basic structure
• nominal level, of data
  • about / The nominal level, What data is like at the nominal level
  • mathematical operations / Mathematical operations allowed
  • measure of center / Measures of center
• normalizing constant
  • about / Naïve Bayes classification
• notation / Probability
• null hypothesis
  • about / Hypothesis tests
• null model
  • about / Regression metrics
• null set / Set theory

O

• odds
  • about / Probability, odds, and log odds
• one-tailed test / Assumptions of the one sample t-tests
• one sample t-test
  • about / One sample t-tests
  • example / Example of a one sample t-tests
  • assumptions / Assumptions of the one sample t-tests
• optimal number
  • selecting, for cluster validation / Choosing an optimal number for K and cluster validation
• ordinal level, of data
  • about / The ordinal level, Quick recap and check
  • examples / Examples
  • mathematical operations / Mathematical operations allowed
  • measures of center / Measures of center
• organized data
  • about / Basic terminology
• overfitting
  • about / Regression metrics
  / Overfitting

P

• p-value
  • about / Hypothesis tests
• parameter
  • about / What are statistics?
• pattern recognition / Basic structure
• perceptron / Basic structure
• perceptrons / Basic structure
• point estimates
  • about / Point estimates
• Poisson distribution
  • about / Point estimates
• Poisson random variable
  • about / Poisson random variable,, Point estimates
  • examples / Poisson random variable,
  • call center example / Poisson random variable,
• population
  • about / What are statistics?
• posterior
  • about / Naïve Bayes classification
• prediction / Supervised learning
• predictive analytics models / Supervised learning
• presentation, to formal audience
  • tips / On the more formal side of things
• Principal Component Analysis (PCA) / Feature extraction and principal component analysis
  • about / Feature extraction and principal component analysis
• prior probability
  • about / Naïve Bayes classification
• probabilistic model
  • about / Some more terminology
• probability
  • about / Probability, Probability, odds, and log odds
• probability, rules
  • about / The rules of probability
  • addition rule / The addition rule
  • mutual exclusivity / Mutual exclusivity
  • multiplication rule / The multiplication rule
  • independence / Independence
  • complementary events / Complementary events
• probability density function (PDF)
  • about / Continuous random variables
• probability mass function (PMF) / Binomial random variables
• probability mass functions (PMF)
  • about / Discrete random variables
• probability sampling / Probability sampling
• procedure
  • about / Basic definitions
• proportional
  • about / Proportional
• Python
  • need for / Why Python?
  • practices / Python practices

Q

• qualitative data
  • about / Quantitative versus qualitative data
• qualitative data, versus quantitative data
  • about / Quantitative versus qualitative data
  • coffee shop data example / Example – coffee shop data
  • world alcohol consumption data example / Example – world alcohol consumption data
• quantitative data
  • about / Quantitative versus qualitative data
  • discrete data / Digging deeper
  • continuous data / Digging deeper

R

• random forests
  • about / Random forests
  • versus decision trees / Comparing Random forests with decision trees
  • advantages / Comparing Random forests with decision trees
  • disadvantages / Comparing Random forests with decision trees
• random sampling / Random sampling
• random variables
  • about / Random variables
  • discrete random variable / Discrete random variables
• ratio level, of data
  • about / The ratio level
  • examples / Examples
  • measures of center / Measures of center
  • issues / Problems with the ratio level
• regression
  • about / Regression
• regression metrics
  • about / Regression metrics
• regression tree
  • building / How does a computer build a regression tree?
• reinforcement learning
  • about / Reinforcement learning, Overview of the types of machine learning
  • pros / Overview of the types of machine learning
  • cons / Overview of the types of machine learning
• relative frequency
  • about / Frequentist approach
• relative length
  • about / Relative length of text

S

• sample
  • about / What are statistics?
• sample space
  • about / Basic definitions
• sampling bias / Random sampling
• sampling distributions
  • about / Sampling distributions
• scalar
  • about / Dot product
• scatter plot
  • about / Scatter plots
• set / Set theory
• set theory / Set theory
• Silhouette Coefficient
  • about / The Silhouette Coefficient
• Simpson's paradox / Simpson's paradox
• slope
  • about / Graphs
• spawner-recruit models
  • example / Example – spawner-recruit models
• square matrix
  • about / Vectors and matrices
• standard deviation / Standard deviation
  • about / Measures of variation
• standard normal distribution
  • about / Continuous random variables
• statistical model
  • about / Some more terminology
• statistical modeling
  • about / How does statistical modeling fit into all of this?
• statistics
  • about / What are statistics?
• statistics, measuring
  • about / How do we measure statistics?
  • measures of center / Measures of center
  • measures of variation / Measures of variation
  • measure of relative standing / Measures of relative standing
• stock prices prediction based on social media case study
  • about / Case study 1 – predicting stock prices based on social media
  • text sentiment analysis / Text sentiment analysis
  • exploratory data analysis / Exploratory data analysis
  • regression route / Regression route
  • classification route / Classification route
  • example / Going beyond with this example
• structured data
  • about / Structured versus unstructured data
  • versus unstructured data / Structured versus unstructured data
• subset / Set theory
• Substantive Expertise / The data science Venn diagram
• summation
  • about / Summation
• sum of squared residuals
  • about / Linear regression
• supervised learning
  • about / Supervised learning
  • working / Supervised learning
  • example / Supervised learning
  • predictions / It's not only about predictions
  • types / Types of supervised learning
  • regression / Regression
  • classification / Classification
  • pros / Overview of the types of machine learning
  • cons / Overview of the types of machine learning

T

• tensorflow case study
  • about / Case study 3 – using tensorflow
  • neural networks, creating / Tensorflow and neural networks
• test statistic
  • about / Assumptions of the one sample t-tests
• titanic dataset / Dataset 2 – titanic
• training error visualization
  • versus cross validation error / Visualizing training error versus cross-validation error
• true negatives / A bit deeper
• true positives / A bit deeper
• Type I error / A bit deeper
• type I error / Type I and type II errors
• type II error / Type I and type II errors
• Type II error / A bit deeper

U

• underfitting / Underfitting
• unequal probability sampling / Unequal probability sampling
• union / Set theory
• unorganized data
  • about / Basic terminology
• unstructured data
  • about / Structured versus unstructured data
  • versus structured data / Structured versus unstructured data
• unsupervised learning
  • about / Unsupervised learning, Overview of the types of machine learning, Unsupervised learning
  • reinforcement learning / Reinforcement learning
  • pros / Overview of the types of machine learning
  • cons / Overview of the types of machine learning
  • using / When to use unsupervised learning

V

• vector
  • about / Vectors and matrices
• verbal communication
  • about / Verbal communication
  • story telling / It's about telling a story

W

• why/how/what strategy, of presentation
  • about / The why/how/what strategy of presenting

Y

• yelp dataset
  • Dataframe / Dataframes
  • Series object / Series

Z

• z-score / Measures of relative standing