Index

A

• agglomerative hierarchical clustering / Hierarchical clustering
• aggregation operations
  • about / Aggregation operations
  • average / Aggregation operations
  • SUM / Aggregation operations
  • MAX / Aggregation operations
  • MIN / Aggregation operations
  • STD / Aggregation operations
  • COUNT / Aggregation operations
• ANOVA
  • about / ANOVA
• Apache Spark
  • about / Python with Apache Spark
  • Python with / Python with Apache Spark
  • installing, URL / Python with Apache Spark
  • sentiment, scoring / Scoring the sentiment
  • overall sentiment / The overall sentiment
• area plot
  • about / Area plots
  • example / Area plots
• array
  • with NumPy / The world of arrays with NumPy
  • creating / Creating an array
  • subtraction / Array subtraction
  • squaring / Squaring an array
  • trigonometric function / A trigonometric function performed on the array
  • conditional operations / Conditional operations
  • matrix multiplication / Matrix multiplication
  • slicing / Indexing and slicing
  • indexing / Indexing and slicing

B

• Bernoulli distribution
  • about / A Bernoulli distribution
• box plot
  • about / Box plots
  • example / Box plots
• bubble chart
  • about / Bubble charts

C

• census income dataset
  • about / The census income dataset
  • exploring / Exploring the census data
  • people histogram, creating / Hypothesis 1: People who are older earn more
  • earning bias, working class based / Hypothesis 2: Income bias based on working class
  • earning power, education based / Hypothesis 3: People with more education earn more
  • earning power, marital status based / Hypothesis 4: Married people tend to earn more
  • earning power, race based / Hypothesis 5: There is a bias in income based on race
  • earning power, occupation based / Hypothesis 6: There is a bias in the income based on occupation
  • earning power, gender based / Hypothesis 7: Men earn more
  • earning power, productive hours based / Hypothesis 8: People who clock in more hours earn more
  • earning power, native countries based / Hypothesis 9: There is a bias in income based on the country of origin
• chart
  • line properties, controlling / Controlling the line properties of a chart
  • text, adding / Playing with text
• chi-square distribution
  • about / The chi-square distribution
• chi-square test
  • for goodness / Chi-square for the goodness of fit
  • of independence / The chi-square test of independence
• classification trees
  • about / Decision trees
• collaborative filtering
  • user-based collaborative filtering / User-based collaborative filtering
  • item-based collaborative filtering / Item-based collaborative filtering
• conditional operations
  • about / Conditional operations
• confidence interval
  • about / A confidence interval
• consumer key
  • URL / Performing sentiment analysis on world leaders using Twitter
• correlation
  • about / Correlation
• CSV
  • about / CSV

D

• 3D plot
  • plotting / A 3D plot of a surface
• data
  • exporting / Inserting and exporting data
  • importing / Inserting and exporting data
  • inserting / Inserting and exporting data
  • preprocessing / Preprocessing data
• data, cleansing
  • data, merging / Merging data
• database
  • data, reading from / Database
• data cleansing
  • about / Data cleansing
  • missing data, checking / Checking the missing data
  • missing data, filling / Filling the missing data
  • string operation / String operations
• DataFrame
  • about / DataFrame
• data journalism website
  • about / Styling your plots
• data mining
  • about / What is data mining?
  • analysis, presenting / Presenting an analysis
• data operations
  • aggregation operations / Aggregation operations
  • joins / Joins
• decision trees
  • about / Decision trees, Decision trees
  • classification trees / Decision trees
  • regression trees / Decision trees
• distribution
  • forms / Various forms of distribution
  • normal distribution / A normal distribution
  • normal distribution, from binomial distribution / A normal distribution from a binomial distribution
  • Poisson distribution / A Poisson distribution
  • Bernoulli distribution / A Bernoulli distribution
• divisive hierarchical clustering / Hierarchical clustering

E

• elbow curve / Determining the number of clusters
• euclidean distance / Determining the number of clusters
• Euclidean distance score
  • about / The Euclidean distance score

F

• Fast Moving Consumer Goods (FMCG) / What is data mining?
• F distribution
  • about / The F distribution
• full outer join / The full outer join

G

• groupby function / The groupby function

H

• Hadoop
  • about / What is Hadoop?
  • programming model / The programming model
  • MapReduce, architecture / The MapReduce architecture
  • DFS / The Hadoop DFS
  • DFS, architecture / Hadoop's DFS architecture
  • URL / Python MapReduce
• Hadoopy
  • used, for file handling / File handling with Hadoopy
  • URL / File handling with Hadoopy
• heatmap
  • about / Heatmaps
  • creating / Heatmaps
• hexagon bin plot
  • about / Hexagon bin plots
• hierarchical clustering
  • about / Hierarchical clustering
  • agglomerative hierarchical clustering / Hierarchical clustering
  • divisive hierarchical clustering / Hierarchical clustering
• histograms
  • combining, with scatter plot / Scatter plots with histograms

I

• inner join / The inner join
• item-based collaborative filtering
  • about / Item-based collaborative filtering

J

• joins
  • about / Joins
  • inner join / The inner join
  • left outer join / The left outer join
  • full outer join / The full outer join
  • groupby function / The groupby function
• JSON
  • about / JSON

K

• k-means clustering
  • about / The k-means clustering, The k-means algorithm and its working
  • URL / The k-means algorithm and its working
  • example / A simple example
• k-means clustering, with countries
  • about / The k-means clustering with countries
  • number of clusters, determining / Determining the number of clusters
  • applying / Clustering the countries
• Kaggle
  • URL / Summary
• keyword arguments
  • used, for controlling line properties of chart / Using keyword arguments

L

• left outer join / The left outer join
• lemmatization
  • about / Stemming and lemmatization, Lemmatization
• linear regression
  • about / Linear regression
  • simple linear regression / Simple linear regression
  • multiple linear regression / Multiple regression
• linear regression model
  • building, with statsmodels module / Training and testing a model
  • building, with SciKit package / Training and testing a model
• line properties, chart
  • controlling / Controlling the line properties of a chart
  • controlling, with keyword arguments / Using keyword arguments
  • controlling, with setter methods / Using the setter methods
  • controlling, with setp() command / Using the setp() command
• logistic regression
  • about / Logistic regression, Logistic regression
  • data, preparing / Data preparation
  • training, creating / Creating training and testing sets
  • sets, testing / Creating training and testing sets
  • model, building / Building a model, Model building and evaluation with SciKit
  • model, evaluating / Model evaluation
  • model evaluating, test data based / Evaluating a model based on test data
  • model, evaluating with SciKit / Model building and evaluation with SciKit

M

• machine learning
  • Andrew NG course, URL / Summary
• machine learning, types
  • about / Different types of machine learning
  • supervised learning / Different types of machine learning
  • unsupervised learning / Different types of machine learning
  • reinforcement learning / Different types of machine learning
• MapReduce
  • about / The MapReduce architecture
  • Python used / Python MapReduce
  • word count / The basic word count
  • sentiment score, for review / A sentiment score for each review
  • overall sentiment score / The overall sentiment score
  • code, deploying on Hadoop / Deploying the MapReduce code on Hadoop
• mathematical operations
  • about / Mathematical operations
• matrix multiplication
  • about / Matrix multiplication
• model
  • training / Training and testing a model
  • testing / Training and testing a model
• multiple linear regression
  • about / Multiple regression
  • example / Multiple regression
• multiple plots
  • creating / Creating multiple plots

N

• naive Bayes classifier
  • about / The naive Bayes classifier
• Natural Language Toolkit (NLTK)
  • URL / Preprocessing data
• normal distribution
  • about / A normal distribution
  • from binomial distribution / A normal distribution from a binomial distribution
• null hypothesis
  • about / A p-value
• NumPy array
  • about / The world of arrays with NumPy
• NumPy documentation
  • URL / Summary

O

• one-tailed tests
  • about / One-tailed and two-tailed tests
• Ordinary Least Square Regression (OLS)
  • about / Training and testing a model

P

• P-value
  • about / A p-value
• pandas, data structure
  • about / The data structure of pandas
  • series / Series
  • DataFrame / DataFrame
  • panel / Panel
• pandas documentation
  • URL / Summary
• pandas library
  • about / Empowering data analysis with pandas
• panel
  • about / Panel
• parts of speech tagging
  • about / Parts of speech tagging
• Pearson correlation score
  • about / The Pearson correlation score
• Pig
  • about / Pig
• Pig Latin
  • URL / Pig
• plots
  • styling / Styling your plots
• Poisson distribution
  • about / A Poisson distribution
• PunktSentenceTokenizer / Word and sentence tokenization

R

• random forests
  • about / Random forests
• RDDs (Resilient Distributed Datasets) / Python with Apache Spark
• recommendation data
  • about / Recommendation data
• regression trees
  • about / Decision trees
• reinforcement learning
  • about / Reinforcement learning

S

• scatter plot
  • with histograms / Scatter plots with histograms
• scatter plot matrix
  • about / A scatter plot matrix
• SciKit package
  • used, for building linear regression model / Training and testing a model
• SciPy package
  • URL / A normal distribution from a binomial distribution
• sentence tokenization / Word and sentence tokenization
• Sentiment Analysis
  • on world leaders, Twitter used / Performing sentiment analysis on world leaders using Twitter
• sentiments
  • URL / Performing sentiment analysis on world leaders using Twitter
• series
  • about / Series
• setp() command
  • used, for controlling line properties of chart / Using the setp() command
• setter methods
  • used, for controlling line properties of chart / Using the setter methods
• shape
  • manipulating / Shape manipulation
• simple linear regression
  • about / Simple linear regression
  • example / Simple linear regression
• Stanford Named Entity Recognizer
  • about / The Stanford Named Entity Recognizer
  • URL / The Stanford Named Entity Recognizer
• statsmodels module
  • used, for building linear regression model / Training and testing a model
  • about / Training and testing a model
• stemming
  • about / Stemming and lemmatization, Stemming
• string operation
  • substring / String operations
  • filtering / String operations
  • uppercase / String operations
  • lowercase / String operations
  • length / String operations
  • split / String operations
  • replace / String operations
• supervised learning
  • about / Supervised learning

T

• T-test
  • versus Z-test / Z-test vs T-test
• tags
  • URL / Parts of speech tagging
• text
  • adding, to chart / Playing with text
• Titanic survivors dataset
  • about / Studying the Titanic
  • passenger class survivors, determining / Which passenger class has the maximum number of survivors?
  • survivors distributions, determining based on gender / What is the distribution of survivors based on gender among the various classes?
  • nonsurvivors distributions, determining / What is the distribution of nonsurvivors among the various classes who have family aboard the ship?
  • survival percentage, searching among age groups / What was the survival percentage among different age groups?
• Trellis plot
  • about / Trellis plots
  • example / Trellis plots
• trigonometric function
  • on array / A trigonometric function performed on the array
• two-tailed tests
  • about / One-tailed and two-tailed tests
• Twython package
  • URL / Performing sentiment analysis on world leaders using Twitter
• Type 1 error
  • about / Type 1 and Type 2 errors
• Type 2 error
  • about / Type 1 and Type 2 errors

U

• unsupervised learning
  • about / Unsupervised learning
• user-based collaborative filtering
  • about / User-based collaborative filtering
  • similar users, finding / Finding similar users
  • Euclidean distance score / The Euclidean distance score
  • Pearson correlation score / The Pearson correlation score
  • users, ranking / Ranking the users
  • items, recommending / Recommending items

W

• wordcloud
  • creating / Creating a wordcloud
  • URL / Creating a wordcloud
• word tokenization / Word and sentence tokenization

X

• XLS
  • about / XLS

Z

• z-score
  • about / A z-score
• Z-test
  • versus T-test / Z-test vs T-test