[Dummies 01] • Python® for Data Science For Dummies · 2nd Edition, 2nd Edition by Mueller, John Paul -- Read -- Imperial Library of Trantor

Index

Cover Table of Contents 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

Defining the Sexiest Job of the 21st Century 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

Why Python? 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

Considering the Off-the-Shelf Cross-Platform Scientific Distributions 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 Getting a Google Account 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: Understanding the Tools

Using the Jupyter Console 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: Conditioning 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: Shaping Data

Working with HTML Pages Working with Raw Text Using the Bag of Words Model and Beyond Working with Graph Data

Chapter 9: Putting What You Know in 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, 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 Performing the Hashing Trick 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 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 Making Machine Learning Accessible Boosting Predictions

Part 6: The Part of Tens

Chapter 21: Ten Essential Data Resources

Discovering the News with Subreddit Getting a Good Start with KDnuggets Locating Free Learning Resources with Quora Gaining Insights with Oracle’s Data Science Blog Accessing the Huge List of Resources on Data Science Central Learning New Tricks from the Aspirational Data Scientist Obtaining the Most Authoritative Sources at Udacity Receiving Help with Advanced Topics at Conductrics Obtaining the Facts of Open Source Data Science from Masters 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 E-mails Working with Handwritten Information Working with Pictures Analyzing Amazon.com Reviews Interacting with a Huge Graph

Index About the Authors Advertisement Page Connect with Dummies End User License Agreement

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