(Ebook) Doing Data Science 1st Edition by Cathy O Neil, Rachel Schutt ISBN 1449358659 9781449358655

(Ebook) Doing Data Science 1st Edition by Cathy O Neil, Rachel Schutt ISBN 1449358659 9781449358655

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ISBN 10: 1449358659 
ISBN 13: 9781449358655
Author: Cathy O Neil, Rachel Schutt

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

(Ebook) Doing Data Science 1st Table of contents:

Chapter 1. Introduction: What Is Data Science?
Big Data and Data Science Hype
Getting Past the Hype
Why Now?
Datafication
The Current Landscape (with a Little History)
Data Science Jobs
A Data Science Profile
Thought Experiment: Meta-Definition
OK, So What Is a Data Scientist, Really?
In Academia
In Industry
Chapter 2. Statistical Inference, Exploratory Data Analysis, and the Data Science Process
Statistical Thinking in the Age of Big Data
Statistical Inference
Populations and Samples
Populations and Samples of Big Data
Big Data Can Mean Big Assumptions
Modeling
Exploratory Data Analysis
Philosophy of Exploratory Data Analysis
Exercise: EDA
The Data Science Process
A Data Scientist’s Role in This Process
Thought Experiment: How Would You Simulate Chaos?
Case Study: RealDirect
How Does RealDirect Make Money?
Exercise: RealDirect Data Strategy
Chapter 3. Algorithms
Machine Learning Algorithms
Three Basic Algorithms
Linear Regression
k-Nearest Neighbors (k-NN)
k-means
Exercise: Basic Machine Learning Algorithms
Solutions
Summing It All Up
Thought Experiment: Automated Statistician
Chapter 4. Spam Filters, Naive Bayes, and Wrangling
Thought Experiment: Learning by Example
Why Won’t Linear Regression Work for Filtering Spam?
How About k-nearest Neighbors?
Naive Bayes
Bayes Law
A Spam Filter for Individual Words
A Spam Filter That Combines Words: Naive Bayes
Fancy It Up: Laplace Smoothing
Comparing Naive Bayes to k-NN
Sample Code in bash
Scraping the Web: APIs and Other Tools
Jake’s Exercise: Naive Bayes for Article Classification
Sample R Code for Dealing with the NYT API
Chapter 5. Logistic Regression
Thought Experiments
Classifiers
Runtime
You
Interpretability
Scalability
M6D Logistic Regression Case Study
Click Models
The Underlying Math
Estimating α and β
Newton’s Method
Stochastic Gradient Descent
Implementation
Evaluation
Media 6 Degrees Exercise
Sample R Code
Chapter 6. Time Stamps and Financial Modeling
Kyle Teague and GetGlue
Timestamps
Exploratory Data Analysis (EDA)
Metrics and New Variables or Features
What’s Next?
Cathy O’Neil
Thought Experiment
Financial Modeling
In-Sample, Out-of-Sample, and Causality
Preparing Financial Data
Log Returns
Example: The S&P Index
Working out a Volatility Measurement
Exponential Downweighting
The Financial Modeling Feedback Loop
Why Regression?
Adding Priors
A Baby Model
Exercise: GetGlue and Timestamped Event Data
Exercise: Financial Data
Chapter 7. Extracting Meaning from Data
William Cukierski
Background: Data Science Competitions
Background: Crowdsourcing
The Kaggle Model
A Single Contestant
Their Customers
Thought Experiment: What Are the Ethical Implications of a Robo-Grader?
Feature Selection

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