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• Cover
• Title Page
• Copyright
• Introduction
• About This Book
• Foolish Assumptions
• Icons Used in This Book
• Beyond the Book
• Where to Go from Here
• Part 1: Getting Started with Data Science and Python
• Chapter 1: Discovering the Match between Data Science and Python
• Understanding Python as a Language
• Defining Data Science
• Creating the Data Science Pipeline
• Understanding Python’s Role in Data Science
• Learning to Use Python Fast
• Chapter 2: Introducing Python’s Capabilities and Wonders
• Working with Python
• Performing Rapid Prototyping and Experimentation
• Considering Speed of Execution
• Visualizing Power
• Using the Python Ecosystem for Data Science
• Chapter 3: Setting Up Python for Data Science
• Working with Anaconda
• Installing Anaconda on Windows
• Installing Anaconda on Linux
• Installing Anaconda on Mac OS X
• Downloading the Datasets and Example Code
• Chapter 4: Working with Google Colab
• Defining Google Colab
• Working with Notebooks
• Performing Common Tasks
• Using Hardware Acceleration
• Executing the Code
• Viewing Your Notebook
• Sharing Your Notebook
• Getting Help
• Part 2: Getting Your Hands Dirty with Data
• Chapter 5: Working with Jupyter Notebook
• Using Jupyter Notebook
• Performing Multimedia and Graphic Integration
• Chapter 6: Working with Real Data
• Uploading, Streaming, and Sampling Data
• Accessing Data in Structured Flat-File Form
• Sending Data in Unstructured File Form
• Managing Data from Relational Databases
• Interacting with Data from NoSQL Databases
• Accessing Data from the Web
• Chapter 7: Processing Your Data
• Juggling between NumPy and pandas
• Validating Your Data
• Manipulating Categorical Variables
• Dealing with Dates in Your Data
• Dealing with Missing Data
• Slicing and Dicing: Filtering and Selecting Data
• Concatenating and Transforming
• Aggregating Data at Any Level
• Chapter 8: Reshaping Data
• Using the Bag of Words Model to Tokenize Data
• Working with Graph Data
• Chapter 9: Putting What You Know into Action
• Contextualizing Problems and Data
• Considering the Art of Feature Creation
• Performing Operations on Arrays
• Part 3: Visualizing Information
• Chapter 10: Getting a Crash Course in Matplotlib
• Starting with a Graph
• Setting the Axis, Ticks, and Grids
• Defining the Line Appearance
• Using Labels, Annotations, and Legends
• Chapter 11: Visualizing the Data
• Choosing the Right Graph
• Creating Advanced Scatterplots
• Plotting Time Series
• Plotting Geographical Data
• Visualizing Graphs
• Part 4: Wrangling Data
• Chapter 12: Stretching Python’s Capabilities
• Playing with Scikit-learn
• Using Transformative Functions
• Considering Timing and Performance
• Running in Parallel on Multiple Cores
• Chapter 13: Exploring Data Analysis
• The EDA Approach
• Defining Descriptive Statistics for Numeric Data
• Counting for Categorical Data
• Creating Applied Visualization for EDA
• Understanding Correlation
• Working with Cramér’s V
• Modifying Data Distributions
• Chapter 14: Reducing Dimensionality
• Understanding SVD
• Performing Factor Analysis and PCA
• Understanding Some Applications
• Chapter 15: Clustering
• Clustering with K-means
• Performing Hierarchical Clustering
• Discovering New Groups with DBScan
• Chapter 16: Detecting Outliers in Data
• Considering Outlier Detection
• Examining a Simple Univariate Method
• Developing a Multivariate Approach
• Part 5: Learning from Data
• Chapter 17: Exploring Four Simple and Effective Algorithms
• Guessing the Number: Linear Regression
• Moving to Logistic Regression
• Making Things as Simple as Naïve Bayes
• Learning Lazily with Nearest Neighbors
• Chapter 18: Performing Cross-Validation, Selection, and Optimization
• Pondering the Problem of Fitting a Model
• Cross-Validating
• Selecting Variables Like a Pro
• Pumping Up Your Hyperparameters
• Chapter 19: Increasing Complexity with Linear and Nonlinear Tricks
• Using Nonlinear Transformations
• Regularizing Linear Models
• Fighting with Big Data Chunk by Chunk
• Understanding Support Vector Machines
• Playing with Neural Networks
• Chapter 20: Understanding the Power of the Many
• Starting with a Plain Decision Tree
• Getting Lost in a Random Forest
• Boosting Predictions
• Part 6: The Part of Tens
• Chapter 21: Ten Essential Data Resources
• Discovering the News with Reddit
• Getting a Good Start with KDnuggets
• Locating Free Learning Resources with Quora
• Gaining Insights with Oracle’s AI & Data Science Blog
• Accessing the Huge List of Resources on Data Science Central
• Discovering New Beginner Data Science Methodologies at Data Science 101
• Obtaining the Most Authoritative Sources at Udacity
• Receiving Help with Advanced Topics at Conductrics
• Obtaining the Facts of Open Source Data Science from Springboard
• Zeroing In on Developer Resources with Jonathan Bower
• Chapter 22: Ten Data Challenges You Should Take
• Removing Personally Identifiable Information
• Creating a Secure Data Environment
• Working with a Multiple-Data-Source Problem
• Honing Your Overfit Strategies
• Trudging Through the MovieLens Dataset
• Locating the Correct Data Source
• Working with Handwritten Information
• Working with Pictures
• Indentifying Data Lineage
• Interacting with a Huge Graph
• Index
• About the Authors
• Connect with Dummies
• End User License Agreement