Python for Data Science for Dummies

Title Page
Copyright Page
Table of Contents
Introduction
	About This Book
	Foolish Assumptions
	Icons Used in This Book
	Beyond the Book
	Where to Go from Here
Part I Getting Started with Python for Data Science
	Chapter 1 Discovering the Match between Data Science and Python
		Defining the Sexiest Job of the 21st Century
			Considering the emergence of data science
			Outlining the core competencies of a data scientist
			Linking data science and big data
			Understanding the role of programming
		Creating the Data Science Pipeline
			Preparing the data
			Performing exploratory data analysis
			Learning from data
			Visualizing
			Obtaining insights and data products
		Understanding Python’s Role in Data Science
			Considering the shifting profile of data scientists
			Working with a multipurpose, simple, and efficient language
		Learning to Use Python Fast
			Loading data
			Training a model
			Viewing a result
	Chapter 2 Introducing Python’s Capabilities and Wonders
		Why Python?
			Grasping Python’s core philosophy
			Discovering present and future development goals
		Working with Python
			Getting a taste of the language
			Understanding the need for indentation
			Working at the command line or in the IDE
		Performing Rapid Prototyping and Experimentation
		Considering Speed of Execution
		Visualizing Power
		Using the Python Ecosystem for Data Science
			Accessing scientific tools using SciPy
			Performing fundamental scientific computing using NumPy
			Performing data analysis using pandas
			Implementing machine learning using Scikit‐learn
			Plotting the data using matplotlib
			Parsing HTML documents using Beautiful Soup
	Chapter 3 Setting Up Python for Data Science
		Considering the Off‐the‐Shelf Cross‐Platform Scientific Distributions
			Getting Continuum Analytics Anaconda
			Getting Enthought Canopy Express
			Getting pythonxy
			Getting WinPython
		Installing Anaconda on Windows
		Installing Anaconda on Linux
		Installing Anaconda on Mac OS X
		Downloading the Datasets and Example Code
			Using IPython Notebook
			Defining the code repository
			Understanding the datasets used in this book
	Chapter 4 Reviewing Basic Python
		Working with Numbers and Logic
			Performing variable assignments
			Doing arithmetic
			Comparing data using Boolean expressions
		Creating and Using Strings
		Interacting with Dates
		Creating and Using Functions
			Creating reusable functions
			Calling functions in a variety of ways
		Using Conditional and Loop Statements
			Making decisions using the if statement
			Choosing between multiple options using nested decisions
			Performing repetitive tasks using for
			Using the while statement
		Storing Data Using Sets, Lists, and Tuples
			Performing operations on sets
			Working with lists
			Creating and using Tuples
		Defining Useful Iterators
		Indexing Data Using Dictionaries
Part II Getting Your Hands Dirty with Data
	Chapter 5 Working with Real Data
		Uploading, Streaming, and Sampling Data
			Uploading small amounts of data into memory
			Streaming large amounts of data into memory
			Sampling data
		Accessing Data in Structured Flat‐File Form
			Reading from a text file
			Reading CSV delimited format
			Reading Excel and other Microsoft Office files
		Sending Data in Unstructured File Form
		Managing Data from Relational Databases
		Interacting with Data from NoSQL Databases
		Accessing Data from the Web
	Chapter 6 Conditioning Your Data
		Juggling between NumPy and pandas
			Knowing when to use NumPy
			Knowing when to use pandas
		Validating Your Data
			Figuring out what’s in your data
			Removing duplicates
			Creating a data map and data plan
		Manipulating Categorical Variables
			Creating categorical variables
			Renaming levels
			Combining levels
		Dealing with Dates in Your Data
			Formatting date and time values
			Using the right time transformation
		Dealing with Missing Data
			Finding the missing data
			Encoding missingness
			Imputing missing data
		Slicing and Dicing: Filtering and Selecting Data
			Slicing rows
			Slicing columns
			Dicing
		Concatenating and Transforming
			Adding new cases and variables
			Removing data
			Sorting and shuffling
		Aggregating Data at Any Level
	Chapter 7 Shaping Data
		Working with HTML Pages
			Parsing XML and HTML
			Using XPath for data extraction
		Working with Raw Text
			Dealing with Unicode
			Stemming and removing stop words
			Introducing regular expressions
		Using the Bag of Words Model and Beyond
			Understanding the bag of words model
			Working with n‐grams
			Implementing TF‐IDF transformations
		Working with Graph Data
			Understanding the adjacency matrix
			Using NetworkX basics
	Chapter 8 Putting What You Know in Action
		Contextualizing Problems and Data
			Evaluating a data science problem
			Researching solutions
			Formulating a hypothesis
			Preparing your data
		Considering the Art of Feature Creation
			Defining feature creation
			Combining variables
			Understanding binning and discretization
			Using indicator variables
			Transforming distributions
		Performing Operations on Arrays
			Using vectorization
			Performing simple arithmetic on vectors and matrices
			Performing matrix vector multiplication
			Performing matrix multiplication
Part III Visualizing the Invisible
	Chapter 9 Getting a Crash Course in MatPlotLib
		Starting with a Graph
			Defining the plot
			Drawing multiple lines and plots
			Saving your work
		Setting the Axis, Ticks, Grids
			Getting the axes
			Formatting the axes
			Adding grids
		Defining the Line Appearance
			Working with line styles
			Using colors
			Adding markers
		Using Labels, Annotations, and Legends
			Adding labels
			Annotating the chart
			Creating a legend
	Chapter 10 Visualizing the Data
		Choosing the Right Graph
			Showing parts of a whole with pie charts
			Creating comparisons with bar charts
			Showing distributions using histograms
			Depicting groups using box plots
			Seeing data patterns using scatterplots
		Creating Advanced Scatterplots
			Depicting groups
			Showing correlations
		Plotting Time Series
			Representing time on axes
			Plotting trends over time
		Plotting Geographical Data
		Visualizing Graphs
			Developing undirected graphs
			Developing directed graphs
	Chapter 11 Understanding the Tools
		Using the IPython Console
			Interacting with screen text
			Changing the window appearance
			Getting Python help
			Getting IPython help
			Using magic functions
			Discovering objects
		Using IPython Notebook
			Working with styles
			Restarting the kernel
			Restoring a checkpoint
		Performing Multimedia and Graphic Integration
			Embedding plots and other images
			Loading examples from online sites
			Obtaining online graphics and multimedia
Part IV Wrangling Data
	Chapter 12 Stretching Python’s Capabilities
		Playing with Scikit‐learn
			Understanding classes in Scikit‐learn
			Defining applications for data science
		Performing the Hashing Trick
			Using hash functions
			Demonstrating the hashing trick
			Working with deterministic selection
		Considering Timing and Performance
			Benchmarking with timeit
			Working with the memory profiler
		Running in Parallel
			Performing multicore parallelism
			Demonstrating multiprocessing
	Chapter 13 Exploring Data Analysis
		The EDA Approach
		Defining Descriptive Statistics for Numeric Data
			Measuring central tendency
			Measuring variance and range
			Working with percentiles
			Defining measures of normality
		Counting for Categorical Data
			Understanding frequencies
			Creating contingency tables
		Creating Applied Visualization for EDA
			Inspecting boxplots
			Performing t‐tests after boxplots
			Observing parallel coordinates
			Graphing distributions
			Plotting scatterplots
		Understanding Correlation
			Using covariance and correlation
			Using nonparametric correlation
			Considering chi‐square for tables
		Modifying Data Distributions
			Using the normal distribution
			Creating a Z‐score standardization
			Transforming other notable distributions
	Chapter 14 Reducing Dimensionality
		Understanding SVD
			Looking for dimensionality reduction
			Using SVD to measure the invisible
		Performing Factor and Principal Component Analysis
			Considering the psychometric model
			Looking for hidden factors
			Using components, not factors
			Achieving dimensionality reduction
		Understanding Some Applications
			Recognizing faces with PCA
			Extracting Topics with NMF
			Recommending movies
	Chapter 15 Clustering
		Clustering with K‐means
			Understanding centroid‐based algorithms
			Creating an example with image data
			Looking for optimal solutions
			Clustering big data
		Performing Hierarchical Clustering
		Moving Beyond the Round-Shaped Clusters: DBScan
	Chapter 16 Detecting Outliers in Data
		Considering Detection of Outliers
			Finding more things that can go wrong
			Understanding anomalies and novel data
		Examining a Simple Univariate Method
			Leveraging on the Gaussian distribution
			Making assumptions and checking out
		Developing a Multivariate Approach
			Using principal component analysis
			Using cluster analysis
			Automating outliers detection with SVM
Part V Learning from Data
	Chapter 17 Exploring Four Simple and Effective Algorithms
		Guessing the Number: Linear Regression
			Defining the family of linear models
			Using more variables
			Understanding limitations and problems
		Moving to Logistic Regression
			Applying logistic regression
			Considering when classes are more
		Making Things as Simple as Naïve Bayes
			Finding out that Naïve Bayes isn’t so naïve
			Predicting text classifications
		Learning Lazily with Nearest Neighbors
			Predicting after observing neighbors
			Choosing your k parameter wisely
	Chapter 18 Performing Cross‐Validation, Selection, and Optimization
		Pondering the Problem of Fitting a Model
			Understanding bias and variance
			Defining a strategy for picking models
			Dividing between training and test sets
		Cross‐Validating
			Using cross‐validation on k folds
			Sampling stratifications for complex data
		Selecting Variables Like a Pro
			Selecting by univariate measures
			Using a greedy search
		Pumping Up Your Hyperparameters
			Implementing a grid search
			Trying a randomized search
	Chapter 19 Increasing Complexity with Linear and Nonlinear Tricks
		Using Nonlinear Transformations
			Doing variable transformations
			Creating interactions between variables
		Regularizing Linear Models
			Relying on Ridge regression (L2)
			Using the Lasso (L1)
			Leveraging regularization
			Combining L1 & L2: Elasticnet
		Fighting with Big Data Chunk by Chunk
			Determining when there is too much data
			Implementing Stochastic Gradient Descent
		Understanding Support Vector Machines
			Relying on a computational method
			Fixing many new parameters
			Classifying with SVC
			Going nonlinear is easy
			Performing regression with SVR
			Creating a stochastic solution with SVM
	Chapter 20 Understanding the Power of the Many
		Starting with a Plain Decision Tree
			Understanding a decision tree
			Creating classification and regression trees
		Making Machine Learning Accessible
			Working with a Random Forest classifier
			Working with a Random Forest regressor
			Optimizing a Random Forest
		Boosting Predictions
			Knowing that many weak predictors win
			Creating a gradient boosting classifier
			Creating a gradient boosting regressor
			Using GBM hyper‐parameters
Part VI The Part of Tens
	Chapter 21 Ten Essential Data Science Resource Collections
		Gaining Insights with Data Science Weekly
		Obtaining a Resource List at U Climb Higher
		Getting a Good Start with KDnuggets
		Accessing the Huge List of Resources on Data Science Central
		Obtaining the Facts of Open Source Data Science from Masters
		Locating Free Learning Resources with Quora
		Receiving Help with Advanced Topics at Conductrics
		Learning New Tricks from the Aspirational Data Scientist
		Finding Data Intelligence and Analytics Resources at AnalyticBridge
		Zeroing In on Developer Resources with Jonathan Bower
	Chapter 22 Ten Data Challenges You Should Take
		Meeting the Data Science London + Scikit‐learn Challenge
		Predicting Survival on the Titanic
		Finding a Kaggle Competition that Suits Your Needs
		Honing Your Overfit Strategies
		Trudging Through the MovieLens Dataset
		Getting Rid of Spam Emails
		Working with Handwritten Information
		Working with Pictures
		Analyzing Amazon.com Reviews
		Interacting with a Huge Graph
Index
EULA