Holdings Information

Bibliographic Record Display

• Title:Doing data science / Rachel Schutt and Cathy O'Neil.
  
•    
• Author/Creator:Schutt, Rachel, 1976- author.
  
• Other Contributors/Collections:O'Neil, Cathy, author.
  ebrary, Inc.
  
• Published/Created:Sebastopol, CA : O'Reilly Media, 2013.
  

• Holdings

  Holdings Record Display

  • Location:ONLINEWhere is this?
    
  • Call Number: QA76.9.D343
    
  • Number of Items:
    0
  • Status:No information available 

   
• Library of Congress Subjects:Big data.
  Data mining.
  Information science.
  Data structures (Computer science)
  Database management.
  Cyberinfrastructure.
  
• Subject(s):Electronic books.
  
• Edition:First edition.
  
• Description:1 online resource.
  
• Notes:Includes index.
  Description based on online resource; title from digital title page (viewed on Dec. 16, 2013).
  
• ISBN:1449358659
  9781449358655
  9781449363895 (electronic bk.)
  144936389X (electronic bk.)
  
• Contents:Machine generated contents note: Big Data and Data Science Hype
  Getting Past the Hype
  Why Now?
  Datafication
  Current Landscape (with a Little History)
  Data Science Jobs
  Data Science Profile
  Thought Experiment: Meta-Definition
  OK, So What Is a Data Scientist, Really?
  In Academia
  In Industry
  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
  Data Science Process
  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
  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
  Thought Experiment: Learning by Example
  Why Won't Linear Regression Work for Filtering Spam?
  How About k-nearest Neighbors?
  Naive Bayes
  Bayes Law
  Spam Filter for Individual Words
  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
  Thought Experiments
  Classifiers
  Runtime
  You
  Interpretability
  Scalability
  M6D Logistic Regression Case Study
  Click Models
  Underlying Math
  Estimating α and β
  Newton's Method
  Stochastic Gradient Descent
  Implementation 124,
  Evaluation
  Media 6 Degrees Exercise
  Sample R Code
  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
  Financial Modeling Feedback Loop
  Why Regression?
  Adding Priors
  Baby Model
  Exercise: GetGlue and Timestamped Event Data
  Exercise: Financial Data
  William Cukierski
  Background: Data Science Competitions
  Background: Crowdsourcing
  Kaggle Model
  Single Contestant
  Their Customers
  Thought Experiment: What Are the Ethical Implications of a Robo-Grader?
  Feature Selection
  Example: User Retention
  Filters
  Wrappers
  Embedded Methods: Decision Trees
  Entropy
  Decision Tree Algorithm
  Handling Continuous Variables in Decision Trees
  Random Forests
  User Retention: Interpretability Versus Predictive Power
  David Huffaker: Google's Hybrid Approach to Social Research
  Moving from Descriptive to Predictive
  Social at Google
  Privacy
  Thought Experiment: What Is the Best Way to Decrease Concern and Increase Understanding and Control?
  Real-World Recommendation Engine
  Nearest Neighbor Algorithm Review
  Some Problems with Nearest Neighbors
  Beyond Nearest Neighbor: Machine Learning Classification
  Dimensionality Problem
  Singular Value Decomposition (SVD)
  Important Properties of SVD
  Principal Component Analysis (PCA)
  Alternating Least Squares
  Fix V and Update U
  Last Thoughts on These Algorithms
  Thought Experiment: Filter Bubbles
  Exercise: Build Your Own Recommendation System
  Sample Code in Python
  Data Visualization History
  Gabriel Tarde
  Mark's Thought Experiment
  What Is Data Science, Redux?
  Processing
  Franco Moretti
  Sample of Data Visualization Projects
  Mark's Data Visualization Projects
  New York Times Lobby: Moveable Type
  Project Cascade: Lives on a Screen
  Cronkite Plaza
  eBay Transactions and Books
  Public Theater Shakespeare Machine
  Goals of These Exhibits
  Data Science and Risk
  About Square
  Risk Challenge
  Trouble with Performance Estimation
  Model Building Tips
  Data Visualization at Square
  Ian's Thought Experiment
  Data Visualization for the Rest of Us
  Data Visualization Exercise
  Social Network Analysis at Morning Analytics
  Case-Attribute Data versus Social Network Data
  Social Network Analysis
  Terminology from Social Networks
  Centrality Measures
  Industry of Centrality Measures
  Thought Experiment
  Morningside Analytics
  How Visualizations Help Us Find Schools of Fish
  More Background on Social Network Analysis from a Statistical Point of View
  Representations of Networks and Eigenvalue Centrality
  First Example of Random Graphs: The Erdos-Renyi Model
  Second Example of Random Graphs: The Exponential Random Graph Model
  Data Journalism
  Bit of History on Data Journalism
  Writing Technical Journalism: Advice from an Expert
  Correlation Doesn't Imply Causation
  Asking Causal Questions
  Confounders: A Dating Example
  OK Cupid's Attempt
  Gold Standard: Randomized Clinical Trials
  A/B Tests
  Second Best: Observational Studies
  Simpson's Paradox
  Rubin Causal Model
  Visualizing Causality
  Definition: The Causal Effect
  Three Pieces of Advice
  Madigan's Background
  Thought Experiment
  Modern Academic Statistics
  Medical Literature and Observational Studies
  Stratification Does Not Solve the Confounder Problem
  What Do People Do About Confounding Things in Practice?
  Is There a Better Way?
  Research Experiment (Observational Medical Outcomes Partnership)
  Closing Thought Experiment
  Claudia's Data Scientist Profile
  Life of a Chief Data Scientist
  On Being a Female Data Scientist
  Data Mining Competitions
  How to Be a Good Modeler
  Data Leakage
  Market Predictions
  Amazon Case Study: Big Spenders
  Jewelry Sampling Problem
  IBM Customer Targeting
  Breast Cancer Detection
  Pneumonia Prediction
  How to Avoid Leakage
  Evaluating Models
  Accuracy: Meh
  Probabilities Matter, Not 0s and 1s
  Choosing an Algorithm
  Final Example
  Parting Thoughts
  About David Crawshaw
  Thought Experiment
  MapReduce
  Word Frequency Problem
  Enter MapReduce
  Other Examples of MapReduce
  What Can't MapReduce Do?
  Pregel
  About Josh Wills
  Thought Experiment
  On Being a Data Scientist
  Data Abundance Versus Data Scarcity
  Designing Models
  Economic Interlude: Hadoop
  Brief Introduction to Hadoop
  Cloudera
  Back to Josh: Workflow
  So How to Get Started with Hadoop?
  Process Thinking
  Naive No Longer
  Helping Hands
  Your Mileage May Vary
  Bridging Tunnels
  Some of Our Work
  What Just Happened?
  What Is Data Science (Again)?
  What Are Next-Gen Data Scientists?
  Being Problem Solvers
  Cultivating Soft Skills
  Being Question Askers
  Being an Ethical Data Scientist
  Career Advice.