# Index

## A

- .arc elements / Pie chart
- account, Wakari
- creating / Creating an account in Wakari

- ACM-KDD Cup
- URL / Open data

- aggregate functions
- aggregation, MapReduce
- performing / Grouping and aggregation

- aggregation, Pandas
- aggregation framework, MongoDB
- about / The aggregation framework
- pipelines / Pipelines
- expressions / Expressions
- limitations / Expressions

- algorithms, for classification
- support vector machines / Learning and classification
- neural networks / Learning and classification
- decision trees / Learning and classification
- Naïve Bayes / Learning and classification
- hidden Markov models / Learning and classification

- Analysis ToolPak
- about / Excel files
- URL / Excel files

- ANEW
- animation
- about / Interaction and animation

- Apache Hadoop
- about / What about big data?, MapReduce overview
- URL / MapReduce overview

- Application Programming Interface (API)
- about / Acquiring my Facebook graph

- Artificial Intelligence (AI)
- about / Artificial intelligence (AI)

## B

- bag of words model
- about / Bag of words

- bar chart
- Bartlett window / Smoothing the time series
- basic reproduction ratio / The epidemiology triangle
- Bayesian classification
- about / Bayesian classification

- Bayes theorem
- about / Bayesian classification

- big data
- about / What about big data?
- features / What about big data?
- fundamental idea / What about big data?
- challenges / What about big data?

- bigrams
- about / Text corpus

- binary classification
- about / Learning and classification

- Binomial model
- about / Random walk simulation

- Blackman window / Smoothing the time series
- Book-Crossing Dataset
- URL / Open data

- Brownian motion
- about / Random walk simulation

- BSON (Binary JSON)
- about / Getting started with MongoDB

- BSON specification
- URL / Document

## C

- categorical data
- about / The nature of data

- CBIR tools
- wavelets / Image similarity search
- Fourier analysis / Image similarity search

- cellular automata
- about / Modeling with cellular automata
- cell / Cell, state, grid, and neighborhood
- state / Cell, state, grid, and neighborhood
- grid / Cell, state, grid, and neighborhood
- neighborhood / Cell, state, grid, and neighborhood
- global stochastic contact model / Global stochastic contact model
- SIRS model simulation, with D3.js / Simulation of the SIRS model in CA with D3.js

- cellular automaton (CA)
- about / The SIR model

- Center for Disease Control (CDC) / Introduction to epidemiology
- classification
- about / Learning and classification
- binary classification / Learning and classification
- multiclass classification / Learning and classification

- classifier() function / The algorithm
- classifier accuracy
- about / Classifier accuracy

- clustering
- about / Clustering

- collection, MongoDB
- about / Collection

- computer science
- about / Computer science

- computer vision
- about / Learning and classification

- Continuum Analytics / Getting started with Wakari
- correlation, Pandas
- crop method / Transformations
- CSS
- about / CSS

- CSV
- about / CSV

- CSV file
- parsing, csv module used / Parsing a CSV file with the csv module
- parsing, NumPy used / Parsing a CSV file using NumPy

- c_categories variable / The algorithm
- c_words variable / The algorithm

## D

- D3
- about / Data-Driven Documents (D3)
- HyperText Markup Language (HTML) / HTML
- Document Object Model (DOM) / DOM
- Cascading Style Sheets (CSS) / CSS
- JavaScript / JavaScript
- Scalable Vector Graphics (SVG) / SVG
- interactions / Interaction and animation
- animations / Interaction and animation

- d3.csv method / Bar chart
- D3.js
- about / Why D3.js?, Getting started with D3.js
- features / Why D3.js?
- reference links / Why D3.js?
- URL, for downloading / Getting started with D3.js
- bar chart / Bar chart
- pie chart / Pie chart
- scatter plot / Scatter plot
- single line chart / Single line chart
- multi-line chart / Multi-line chart
- implementing / Implementation in D3.js

- d3.layout.force() function / Graph visualization with D3.js
- d3.layout.pie() function / Pie chart
- d3.svg.arc() function / Pie chart
- data
- about / The nature of data
- categorical / The nature of data
- numerical / The nature of data

- data(data) function / Bar chart, Interaction and animation
- data(pie(data)) function / Pie chart
- Data Analysis
- AI / Artificial intelligence (AI)
- ML / Machine Learning (ML)
- statistics / Statistics
- mathematics / Mathematics
- knowledge domain / Knowledge domain
- data / Data, information, and knowledge
- information / Data, information, and knowledge
- knowledge / Data, information, and knowledge

- data analysis process
- about / The data analysis process
- problem definition / The problem
- data preparation / Data preparation
- data exploration / Data exploration
- predictive modeling / Predictive modeling
- results visualization / Visualization of results

- database, MongoDB
- about / Database

- data cleansing
- about / Data scrubbing

- data formats
- about / Data formats
- CSV / CSV
- JSON / JSON
- XML / XML
- YAML / YAML

- DataFrame
- about / Getting started with Pandas
- used, for working with multivariate dataset / Working with multivariate dataset with DataFrame

- DataFrame documentation
- Datahub
- URL / Open data

- data preparation, MongoDB
- about / Data preparation
- data transformation, OpenRefine used / Data transformation with OpenRefine
- documents, inserting with PyMongo / Inserting documents with PyMongo

- data scrubbing
- about / Data scrubbing
- statistical methods / Statistical methods
- text parsing / Text parsing
- data transformation / Data transformation

- dataset
- about / Datasource
- features / Datasource

- datasets
- about / The nature of data

- data sharing
- with IPython Notebook (NB) / The data

- data sources
- about / Datasource
- dataset / Datasource
- open data / Open data
- text files / Text files
- Excel files / Excel files
- SQL databases / SQL databases
- NoSQL databases / NoSQL databases
- multimedia / Multimedia
- web scraping / Web scraping

- data transformation
- about / Data transformation
- applying / Transforming data

- data visualization
- features / Importance of data visualization

- data visualization, IPython Notebook (NB)
- about / Data visualization

- date format validation
- about / Text parsing

- db.collection.mapReduce() method / Using mongo shell
- DBMS
- about / SQL databases

- DDL
- about / SQL databases

- degree distribution, graph
- about / Degree distribution
- graph histogram, exploring / Histogram of a graph
- centrality, defining / Centrality

- delete method, MongoDB
- about / Insert/Update/Delete

- description-based image retrieval
- about / Image similarity search

- Digital Signal Processing (DSP) / Smoothing the time series
- dimensionality reduction
- performing / Dimensionality reduction
- feature selection / Dimensionality reduction
- feature extraction / Dimensionality reduction
- dimension reduction / Dimensionality reduction

- directed graph
- about / Directed graph

- Distribute / Installing and running IDLE on Windows
- DML
- about / SQL databases

- document, MongoDB
- about / Document

- DOM
- about / DOM

- double spiral problem, SVM
- about / Double spiral problem

- DTW
- about / Dynamic time warping (DTW)
- implementing / Implementing DTW

- Dynamic Time Warping (DTW) / Why mlpy?

## E

- .enter() function / Bar chart, Pie chart
- e-mail subject line tester
- about / E-mail subject line tester
- categories / E-mail subject line tester

- e-mail validation
- about / Text parsing

- elastic matching
- about / Image similarity search

- epidemic models
- about / The epidemic models
- SIR model / The SIR model
- SIRS model / The SIRS model

- epidemiology
- about / Introduction to epidemiology

- epidemiology triangle
- about / The epidemiology triangle
- Agent / The epidemiology triangle
- Host / The epidemiology triangle
- Environment / The epidemiology triangle
- Time / The epidemiology triangle

- ETL / Data transformation
- Euclidian distance
- about / Dynamic time warping (DTW)

- Excel files
- about / Excel files

- explain method
- about / Queries

- exploratory data analysis (EDA)
- about / Principal Component Analysis

## F

- Facebook graph
- acquiring / Acquiring my Facebook graph
- acquiring, Netvizz used / Using Netvizz

- findall() function
- about / Text parsing

- find method
- about / Queries

- findOne method
- about / Queries

- Flat window / Smoothing the time series
- followers, Twitter
- about / Followers

- followers, Twython
- working with / Working with followers

- format function / Multi-line chart
- formats, text files
- CSV / Text files
- TSV / Text files
- XML / Text files
- JSON / Text files

- FTSA
- about / Financial time series

- functionalities, MongoDB
- ad hoc queries / Getting started with MongoDB
- replication / Getting started with MongoDB
- load balancing / Getting started with MongoDB
- aggregation / Getting started with MongoDB
- Map-Reduce / Getting started with MongoDB

## G

- $group aggregation operations
- $max / Expressions
- $min / Expressions
- $avg / Expressions
- $sum / Expressions
- $addToSet / Expressions

- GDF file
- transforming, to JSON format / Transforming GDF to JSON

- g element / Pie chart
- genfromtxt function / Parsing a CSV file using NumPy, Smoothing the time series
- Gephi
- about / Representing graphs with Gephi, Installing and running Gephi
- used, for representing graphs / Representing graphs with Gephi
- URL / Representing graphs with Gephi, Installing and running Gephi
- installing, on Linux / Installing and running Gephi on Linux
- running, on Linux / Installing and running Gephi on Linux
- installing, on Windows / Installing and running Gephi on Windows
- running, on Windows / Installing and running Gephi on Windows

- github repository
- Global stochastic contact model
- about / Global stochastic contact model

- gold prices time series
- smoothing / Smoothing the gold prices time series

- Google Flu Trends (GFT) / Introduction to epidemiology
- Google Flu Trends data
- graph
- about / Structure of a graph
- structure / Structure of a graph
- uses / Structure of a graph
- undirected graph / Undirected graph
- directed graph / Directed graph
- representing, Gephi used / Representing graphs with Gephi
- D3.js visualizations, creating / Graph visualization with D3.js

- graph analytics
- categories / Structure of a graph
- structural algorithms / Structure of a graph
- traversal algorithms / Structure of a graph
- pattern-matching algorithms / Structure of a graph

- graph visualization
- D3.js used / Graph visualization with D3.js

- GREL
- about / Transforming data

- group() function
- about / Text parsing

- groupby method / Grouping, aggregation, and correlation
- group function, MongoDB
- grouping, MapReduce
- performing / Grouping and aggregation

- grouping, Pandas

## H

- Hamming window / Smoothing the time series
- Hanning window / Smoothing the time series
- Hilary Mason. research-quality data sets
- URL / Open data

- histogram
- about / Image histogram

- Historical Exchange Rates log
- URL / Multi-line chart

- historical gold prices
- HTML

## I

- IDLE
- installing, on Ubuntu / Installing and running IDLE on Ubuntu
- running, on Ubuntu / Installing and running IDLE on Ubuntu
- installing, on Windows / Installing and running IDLE on Windows
- running, on Windows / Installing and running IDLE on Windows

- image dataset
- processing / Processing the image dataset

- image filtering
- ImageFilter object
- ImageOps object
- URL / Operations

- image processing, with PIL
- image, opening / Opening an image
- histogram / Image histogram
- filtering / Filtering
- operations / Operations
- image transformations / Transformations

- image processing operations
- invert operation / Operations

- image similarity search
- about / Image similarity search

- image transformations
- about / Transformations

- input collection, MapReduce
- filtering / Filtering the input collection

- insert method, MongoDB
- about / Insert/Update/Delete

- integrate method / Solving ordinary differential equation for the SIR model with SciPy
- interaction
- about / Interaction and animation

- IP address validation
- about / Text parsing

- IPython
- URL / Getting started with Wakari
- about / Multiprocessing with IPython
- multiprocessing / Multiprocessing with IPython

- IPython Notebook (NB)
- about / Getting started with IPython Notebook
- blank notebook, starting / Getting started with IPython Notebook
- data visualization / Data visualization
- sharing / Sharing your Notebook
- data, sharing / The data

## J

- JavaScript
- about / JavaScript

- JavaScript file (.js) / Data-Driven Documents (D3)
- JSON
- about / JSON
- GDF, transforming to / Transforming GDF to JSON

- JSON (JavaScript Object Notation
- about / Why MongoDB?

- JSON (JavaScript Object Notation) / Document
- JSON file
- parsing, json module used / Parsing a JSON file using json module

## K

- Kaggle
- URL / Open data

- kernel functions, SVM
- about / Kernel functions

- Kernel ridge regression
- about / Kernel ridge regression

- Kernel Ridge Regression (KRR) / Why mlpy?
- knlRidge.pred() method / Kernel ridge regression
- knowledge domain
- about / Knowledge domain

## L

- language detection
- about / Learning and classification

- LDA
- about / Linear Discriminant Analysis

- learning
- about / Learning and classification

- learn method / Kernel ridge regression
- line element / Single line chart
- Linux
- OpenRefine, running / Installing and running OpenRefine on Linux
- OpenRefine, installing / Installing and running OpenRefine on Linux
- Gephi, installing / Installing and running Gephi on Linux
- Gephi, running / Installing and running Gephi on Linux

- listdir function
- about / E-mail subject line tester

- list_words() function / The algorithm
- location, Twython
- working with / Working with places and trends

## M

- Machine Learning(ML)
- about / Machine Learning (ML)

- Machine Learning Datasets
- URL / Open data

- Manhattan distance
- about / Dynamic time warping (DTW)

- map function
- about / The map function

- MapReduce
- about / What about big data?, MapReduce overview
- implementations / MapReduce overview
- programming model / Programming model
- using, with MongoDB / Using MapReduce with MongoDB
- input collection, filtering / Filtering the input collection
- grouping, performing / Grouping and aggregation
- aggregation, performing / Grouping and aggregation
- word cloud visualization, in positive tweets / Word cloud visualization of the most common positive words in tweets

- MapReduce, using with MongoDB
- map function / The map function
- reduce function / The reduce function
- Mongo shell, using / Using mongo shell
- UMongo, using / Using UMongo
- PyMongo, using / Using PyMongo

- MapReduce-MPI library
- about / MapReduce overview

- mapReduce command / Using mongo shell
- mapReduce method / Using mongo shell
- mapTest function / Using mongo shell
- massively parallel processing (MPP) data store
- about / What about big data?

- Math.random() function / Scatter plot
- about / Generating random numbers

- mathematics
- about / Mathematics

- matplotlib / Data visualization
- Mike Bostock's reference gallery
- Minkowski distance
- about / Dynamic time warping (DTW)

- mlpy
- about / Why mlpy?, Installing and running mlpy
- features / Why mlpy?
- downloading / Why mlpy?
- URL / Installing and running mlpy
- installing, on Ubuntu / Installing and running mlpy on Ubuntu
- running, on Ubuntu / Installing and running mlpy on Ubuntu
- installing, on Windows / Installing and running mlpy on Windows
- running, on Windows / Installing and running mlpy on Windows

- mlpy.dtw_std function / Implementing DTW
- MongoDB
- about / What about big data?, Why MongoDB?, Getting started with MongoDB, MapReduce overview, Installing and running MongoDB
- reference links / Why MongoDB?
- functionalities / Getting started with MongoDB
- URL / Getting started with MongoDB, MapReduce overview, Installing and running MongoDB
- database / Database
- collection / Collection
- document / Document
- Mongo shell / Mongo shell
- insert method / Insert/Update/Delete
- update method / Insert/Update/Delete
- delete method / Insert/Update/Delete
- queries / Queries
- data preparation / Data preparation
- group / Group
- aggregation framework / The aggregation framework
- features / Installing and running MongoDB
- installing, on Ubuntu / Installing and running MongoDB on Ubuntu
- running, on Ubuntu / Installing and running MongoDB on Ubuntu
- installing, on Windows / Installing and running MongoDB on Windows
- running, on Windows / Installing and running MongoDB on Windows
- Python, connecting with / Connecting Python with MongoDB

- Mongo shell
- about / Mongo shell
- using / Using mongo shell

- Monte Carlo methods
- about / Monte Carlo methods

- multi-line chart
- about / Multi-line chart
- format function / Multi-line chart
- point groups, adding / Multi-line chart
- legend, adding / Multi-line chart

- multiclass classification
- about / Learning and classification

- multimedia
- about / Multimedia
- applications / Multimedia

- multiprocessing, IPython
- about / Multiprocessing with IPython
- Pool class / Pool

- multivariate dataset
- about / Understanding the multivariate dataset
- features / Understanding the multivariate dataset
- distribution / Understanding the multivariate dataset

- multivariate dataset, Pandas
- working with, DataFrame used / Working with multivariate dataset with DataFrame

## N

- Naive Bayes model
- about / Naive Bayes

- NASA
- URL / Open data

- Naïve Bayes algorithm
- about / Naïve Bayes algorithm

- neighborhoods, cellular automata
- Von Neumann / Cell, state, grid, and neighborhood
- Moore / Cell, state, grid, and neighborhood
- Moore Extended / Cell, state, grid, and neighborhood
- Global / Cell, state, grid, and neighborhood

- Netvizz
- using / Using Netvizz

- nextStep function / Simulation of the SIRS model in CA with D3.js
- NLTK
- about / Getting started with Natural Language Toolkit (NLTK)
- URL / Getting started with Natural Language Toolkit (NLTK)
- installing / Getting started with Natural Language Toolkit (NLTK)
- classifiers / Getting started with Natural Language Toolkit (NLTK)
- bag of words model / Bag of words
- Naive Bayes model / Naive Bayes

- nltk.word_tokenize method / Bag of words
- nonlinear regression methods
- reference link / Nonlinear regression

- NoSQL
- about / What about big data?

- NoSQL (Not only SQL)
- about / Why MongoDB?
- URL / NoSQL databases

- NoSQL data stores
- document store / NoSQL databases
- key-value store / NoSQL databases
- graph-based store / NoSQL databases

- Not only SQL (NoSQL)
- about / NoSQL databases
- data stores / NoSQL databases

- numerical data
- about / The nature of data

- numeric facets
- about / Numeric facets

- NumPy / Why Python?
- used, for parsing CSV file / Parsing a CSV file using NumPy
- about / Installing and running NumPy
- URL / Installing and running NumPy
- installing, on Ubuntu / Installing and running NumPy on Ubuntu
- running, on Ubuntu / Installing and running NumPy on Ubuntu
- installing, on Windows / Installing and running NumPy on Windows
- running, on Windows / Installing and running NumPy on Windows

## O

- OAuth
- used, for accessing Twitter API / Using OAuth to access Twitter API

- open data
- OpenRefine / Data preparation
- about / Getting started with OpenRefine, Installing and running OpenRefine
- starting / Getting started with OpenRefine
- text facet / Text facet
- clustering / Clustering
- text filters / Text filters
- numeric facets / Numeric facets
- data, transforming / Transforming data
- data, exporting / Exporting data
- operation history / Operation history
- URL / Installing and running OpenRefine
- installing, on Linux / Installing and running OpenRefine on Linux
- running, on Linux / Installing and running OpenRefine on Linux
- installing, on Windows / Installing and running OpenRefine on Windows
- running, on Windows / Installing and running OpenRefine on Windows

- OrderedDict function / Implementing DTW
- ordinary differential equations (ODE)
- about / The SIR model

## P

- Pandas
- about / Getting started with Pandas
- Series / Getting started with Pandas
- DataFrame / Getting started with Pandas
- URL / Getting started with Pandas
- time series / Working with time series
- multivariate dataset, DataFrame object used / Working with multivariate dataset with DataFrame
- grouping / Grouping, aggregation, and correlation
- aggregation / Grouping, aggregation, and correlation
- correlation / Grouping, aggregation, and correlation

- pandas / Why Python?
- PCA
- about / Principal Component Analysis
- implementing / Principal Component Analysis

- Phoenix system
- about / MapReduce overview

- pie chart
- PIL
- URL / Opening an image

- PIL (Python Image Library) / Getting started with Wakari
- Pillow
- about / Processing the image dataset

- pipeline operators
- Pool class
- predicted value
- contrasting / Contrasting the predicted value

- pred method / Kernel ridge regression
- prepareStep function / Simulation of the SIRS model in CA with D3.js
- Principal Component Analysis (PCA) / Why mlpy?
- probabilistic classification
- about / Bayesian classification

- programming model, MapReduce
- about / Programming model

- PyLab
- about / Data visualization

- PyMongo
- used, for inserting documents / Inserting documents with PyMongo
- using / Using PyMongo

- Python
- about / Why Python?, Installing and running Python 3
- features / Why Python?
- reference link / Why Python?
- reference link, for documentation and examples / Why Python?
- URL / Why Python?, Installing and running Python 3

- Python 3
- libraries / Installing and running Python 3

- Python 3.2
- installing, on Ubuntu / Installing and running Python 3.2 on Ubuntu
- running, on Ubuntu / Installing and running Python 3.2 on Ubuntu
- installing, on Windows / Installing and running Python 3.2 on Windows
- downloading / Installing and running Python 3.2 on Windows
- running, on Windows / Installing and running Python 3.2 on Windows

## Q

- QR code (Quick Response Code)
- about / Sensors and cameras

- qualitative data analysis
- quantitative data analysis
- about / Quantitative versus qualitative data analysis
- measurement levels / Quantitative versus qualitative data analysis

- queries, MongoDB
- about / Queries

## R

- RadViz / Working with multivariate dataset with DataFrame
- radviz method / Working with multivariate dataset with DataFrame
- random numbers
- generating / Generating random numbers

- randomWalk() function
- about / Implementation in D3.js

- random walk simulation
- about / Random walk simulation

- RDBMS
- about / SQL databases

- read_csv method / Working with time series
- reduce function
- about / The reduce function

- reduceTest function / Using mongo shell
- regression analysis
- about / The data – historical gold prices
- nonlinear regression / Nonlinear regression
- Kernel ridge regression / Kernel ridge regression
- gold prices time series, smoothing / Smoothing the gold prices time series
- smoothed time series, predicting / Predicting in the smoothed time series
- predicted value, contrasting / Contrasting the predicted value

- resample method / Working with time series
- reshape method / Simulation of the SIRS model in CA with D3.js
- results, similarity-based image retrieval
- analyzing / Analyzing the results

- RFID (Radio-frequency identification)
- about / Sensors and cameras

- RGB color model
- about / Processing the image dataset

- RGB histogram
- plotting, hist method used / Image histogram

## S

- scatter plots
- about / Scatter plot
- Math.random() function / Scatter plot

- scatter_matrix method / Working with multivariate dataset with DataFrame
- Scientific Data from University of Muenster
- URL / Open data

- SciKit / Why Python?
- SciPy / Why Python?
- URL / Installing and running SciPy
- about / Installing and running SciPy
- installing, on Ubuntu / Installing and running SciPy on Ubuntu
- running, on Ubuntu / Installing and running SciPy on Ubuntu
- installing, on Windows / Installing and running SciPy on Windows
- running, on Windows / Installing and running SciPy on Windows

- search() function
- about / Text parsing

- search, Twython
- performing / Simple search

- search engines
- about / Learning and classification

- seasonal influenza (Flu) data
- selectAll function / Bar chart
- sensors
- using / Sensors and cameras
- RFID (Radio-frequency identification) / Sensors and cameras
- QR code (Quick Response Code) / Sensors and cameras

- Sentiment140 / Text corpus
- URL / Text corpus

- sentiment analysis
- performing, for tweets / Sentiment analysis of tweets

- sentiment classification
- about / Sentiment classification
- general process / Sentiment classification
- ANEW / Affective Norms for English Words
- text corpus / Text corpus

- Series
- about / Getting started with Pandas

- Sharding
- about / Collection

- similarity-based image retrieval
- image similarity search / Image similarity search
- DTW / Dynamic time warping (DTW)
- image dataset, processing / Processing the image dataset
- DTW, implementing / Implementing DTW
- results, analyzing / Analyzing the results

- single line chart
- about / Single line chart
- line element / Single line chart

- SIR model
- about / The SIR model
- ordinary differential equation, solving with SciPy / Solving ordinary differential equation for the SIR model with SciPy

- SIRS model
- about / The SIRS model

- SIRS model simulation
- performing in CA, with D3.js / Simulation of the SIRS model in CA with D3.js

- SIR_model function / Solving ordinary differential equation for the SIR model with SciPy
- smoothed time series
- predicting / Predicting in the smoothed time series

- Smoothing Window / Smoothing the time series
- SNA
- SpamAssassin
- about / E-mail subject line tester

- spam classification
- about / Learning and classification

- spam dataset
- spam text
- about / E-mail subject line tester

- speech recognition
- about / Learning and classification

- SQL
- about / Excel files, SQL databases

- SQL databases
- about / SQL databases

- statistical analysis
- about / Statistical analysis
- male to female ratio / Male to female ratio

- statistical methods, data scrubbing
- about / Statistical methods
- values / Statistical methods

- statistics
- about / Statistics

- statistics function / Simulation of the SIRS model in CA with D3.js
- Support Vector Machines (SVM) / Why mlpy?
- SVG
- about / SVG

- SVM
- about / Getting started with support vector machine
- implementing / Getting started with support vector machine
- kernel functions / Kernel functions
- double spiral problem / Double spiral problem
- implementing, on mlpy / SVM implemented on mlpy

- SVM (Support Vector Machines) / Kernel ridge regression

## T

- text classification
- data / E-mail subject line tester
- algorithm / The algorithm
- classifier accuracy / Classifier accuracy

- text corpus
- about / Text corpus
- unigrams / Text corpus
- bigrams / Text corpus

- text facet
- about / Text facet

- text files
- about / Text files
- formats / Text files

- text filters
- about / Text filters

- text parsing, data scrubbing
- performing / Text parsing

- timelines, Twython
- working with / Working with timelines

- time series
- about / Working with the time series data
- nonlinear time series / Working with the time series data
- linear time series / Working with the time series data
- components / Components of a time series
- smoothing / Smoothing the time series

- time series, Pandas
- working with / Working with time series
- plotting / Working with time series

- time series components
- Trend (T) / Components of a time series
- Seasonality (S) / Components of a time series
- Variability (V) / Components of a time series

- token-based authentication system / Using OAuth to access Twitter API
- training() function / The algorithm
- transform attribute / Bar chart
- transform function / Interaction and animation
- TSA
- tweet
- about / Tweet
- sentiment analysis / Sentiment analysis of tweets

- Twitter API
- accessing, OAuth used / Using OAuth to access Twitter API

- Twitter data anatomy
- about / The anatomy of Twitter data
- tweet / Tweet
- followers / Followers
- trends / Trending topics

- Twitter trends
- about / Trending topics

- Twython
- about / Getting started with Twython
- using / Getting started with Twython
- search, performing / Simple search
- timelines, working with / Working with timelines
- followers, working with / Working with followers
- location, working with / Working with places and trends

## U

- Ubuntu
- Python 3.2, installing / Installing and running Python 3.2 on Ubuntu
- Python 3.2, running / Installing and running Python 3.2 on Ubuntu
- IDLE, installing / Installing and running IDLE on Ubuntu
- IDLE, running / Installing and running IDLE on Ubuntu
- NumPy, installing / Installing and running NumPy on Ubuntu
- NumPy, running / Installing and running NumPy on Ubuntu
- SciPy, installing / Installing and running SciPy on Ubuntu
- SciPy, running / Installing and running SciPy on Ubuntu
- mlpy, installing / Installing and running mlpy on Ubuntu
- mlpy, running / Installing and running mlpy on Ubuntu
- MongoDB, installing / Installing and running MongoDB on Ubuntu
- MongoDB, running / Installing and running MongoDB on Ubuntu
- Umongo, installing / Installing and running Umongo on Ubuntu
- Umongo, running / Installing and running Umongo on Ubuntu

- UMongo
- about / Database, Installing and running UMongo
- using / Using UMongo
- URL / Installing and running UMongo
- features / Installing and running UMongo
- installing, on Ubuntu / Installing and running Umongo on Ubuntu
- running, on Ubuntu / Installing and running Umongo on Ubuntu
- installing, on Windows / Installing and running Umongo on Windows
- running, on Windows / Installing and running Umongo on Windows

- undirected graph
- about / Undirected graph

- unigrams
- about / Text corpus

- United States Government
- URL / Open data

- update function / Simulation of the SIRS model in CA with D3.js
- update method, MongoDB
- about / Insert/Update/Delete

## W

- Wakari
- features / Getting started with Wakari
- account, creating / Creating an account in Wakari
- URL / Creating an account in Wakari
- notebooks, sharing / Sharing your Notebook

- Wakari gallery
- URL / The data

- web scraping
- about / Web scraping
- example / Web scraping

- Windows
- Python 3.2, installing / Installing and running Python 3.2 on Windows
- Python 3.2, running / Installing and running Python 3.2 on Windows
- IDLE, installing / Installing and running IDLE on Windows
- IDLE, running / Installing and running IDLE on Windows
- NumPy, installing / Installing and running NumPy on Windows
- NumPy, running / Installing and running NumPy on Windows
- SciPy, installing / Installing and running SciPy on Windows
- SciPy, running / Installing and running SciPy on Windows
- mlpy, installing / Installing and running mlpy on Windows
- mlpy, running / Installing and running mlpy on Windows
- OpenRefine, installing / Installing and running OpenRefine on Windows
- OpenRefine, running / Installing and running OpenRefine on Windows
- MongoDB, installing / Installing and running MongoDB on Windows
- MongoDB, running / Installing and running MongoDB on Windows
- Umongo, installing / Installing and running Umongo on Windows
- Umongo, running / Installing and running Umongo on Windows

- Wine dataset
- WOEID (Yahoo! Where On Earth ID) / Working with places and trends
- Wolfram-Mathematica / Getting started with Wakari
- word cloud visualization, in positive tweets
- World Bank
- URL / Open data

- World Health Organization
- URL / Open data

- World Wide Web Consortium (W3C)
- URL / XML

## X

- XML
- about / XML

- XML file
- parsing, xml module used / Parsing an XML file in Python using xml module

## Y

- Yahoo! Query Language (YQL) / Working with places and trends
- YAML
- about / YAML

## Z

- Zipfian distribution
- URL / Histogram of a graph