Python for Data Science For Dummies

Cover
Table of Contents
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
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