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An edition of Data science from scratch (2015)

Data science from scratch

first principles with Python

First edition.

by Joel Grus

★★★★★ 5.0 (1 rating)
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16 Currently reading
2 Have read

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★★★★★ 5.0 (1 rating)
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16 Currently reading
2 Have read

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An edition of Data science from scratch (2015)

Data science from scratch

first principles with Python

First edition.

by Joel Grus

★★★★★ 5.0 (1 rating)
166 Want to read
16 Currently reading
2 Have read

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Publish Date

2015

Publisher

O'Reilly Media

Language

English

Pages

311

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Previews available in: English

Edition	Availability
1 Data Science from Scratch: First Principles with Python 2019, O'Reilly Media, Incorporated in English 1492041130 9781492041139	zzzz Locate
2 Data Science from Scratch: First Principles with Python 2015, O'Reilly Media, Incorporated in English 1491904402 9781491904404	zzzz Locate
3 Data Science from Scratch: First Principles with Python 2015, O'Reilly Media, Incorporated in English 1491904399 9781491904398	zzzz Locate
4 Data science from scratch: first principles with Python 2015, O'Reilly Media in English - First edition. 149190142X 9781491901427	aaaa Borrow Listen Locate

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Book Details

Table of Contents

Preface

Page xi

1. Introduction

The Ascendance of Data

What Is Data Science?

Motivating Hypothetical: DataSciencester

Finding Key Connectors

Data Scientists You May Know

Salaries and Experience

Topics of Interest

2. A Crash Course in Python

The Zen of Python

Whitespace Formatting

The Not-So-Basics

List Comprehensions

Generators and Iterators

Regular Expressions

Object-Oriented Programming

Functional Tools

zip and Argument Unpacking

args and kwargs

Welcome to DataSciencester!

For Further Exploration

3. Visualizing Data

For Further Exploration

4. Linear Algebra

For Further Exploration

Describing a Single Set of Data

Central Tendencies

Simpson's Paradox

Some Other Correlational Caveats

Correlation and Causation

For Further Exploration

Dependence and Independence

Conditional Probability

Bayes's Theorem

Random Variables

Continuous Distributions

The Normal Distribution

The Central Limit Theorem

For Further Exploration

7. Hypothesis and Inference

Statistical Hypothesis Testing

Example: Flipping a Coin

Confidence Intervals

Example: Running an A/B Test

Bayesian Inference

For Further Exploration

8. Gradient Descent

The Idea Behind Gradient Descent

Estimating the Gradient

Using the Gradient

Choosing the Right Step Size

Putting It All Together

Stochastic Gradient Descent

For Further Exploration

9. Getting Data

stdin and stdout

The Basics of Text Files

Delimited Files

Scraping the Web

HTML and the Parsing Thereof

Example: O'Reilly Books About Data

Using an Unauthenticated API

Example: Using the Twitter APIs

Getting Credentials

For Further Exploration

10. Working with Data

Exploring Your Data

Exploring One-Dimensional Data

Many Dimensions

Cleaning and Munging

Manipulating Data

Dimensionality Reduction

For Further Exploration

11. Machine Learning

What Is Machine Learning?

Overfitting and Underfitting

The Bias-Variance Trade-off

Feature Extraction and Selection

For Further Exploration

12. k-Nearest Neighbors

Example: Favorite Languages

The Curse of Dimensionality

For Further Exploration

13. Naive Bayes

A Really Dumb Spam Filter

A More Sophisticated Spam Filter

Testing Our Model

For Further Exploration

14. Simple Linear Regression

Using Gradient Descent

Maximum Likelihood Estimation

For Further Exploration

15. Multiple Regression

Further Assumptions of the Least Squares Model

Fitting the Model

Interpreting the Model

Goodness of Fit

Digression: The Bootstrap

Standard Errors of Regression Coefficients

For Further Exploration

16. Logistic Regression

The Logistic Function

Applying the Model

Goodness of Fit

Support Vector Machines

For Further Investigation

17. Decision Trees

What Is a Decision Tree?

The Entropy of a Partition

Creating a Decision Tree

Putting It All Together

For Further Exploration

18. Neural Networks

Feed-Forward Neural Networks

Backpropagation

Example: Defeating a CAPTCHA

For Further Exploration

Example: Meetups

Example: Clustering Colors

Bottom-up Hierarchical Clustering

For Further Exploration

20. Natural Language Processing

An Aside: Gibbs Sampling

For Further Exploration

21. Network Analysis

Betweenness Centrality

Eigenvector Centrality

Matrix Multiplication

Directed Graphs and PageRank

For Further Exploration

22. Recommender Systems

Manual Curation

Recommending What's Popular

User-Based Collaborative Filtering

Item-Based Collaborative Filtering

For Further Exploration

23. Databases and SQL

CREATE TABLE and INSERT

Query Optimization

For Further Exploration

Example: Word Count

MapReduce More Generally

Example: Analyzing Status Updates

Example: Matrix Multiplication

An Aside: Combiners

For Further Exploration

25. Go Forth and Do Data Science

Not from Scratch

Do Data Science

Edition Notes

Includes index.

Subtitle from cover.

Classifications

Library of Congress: QA76.73.P98 G78 2015, QA76.9.D343, QA76.9.D3 G78 2015eb

The Physical Object

Pagination: xvi, 311 pages
Number of pages: 311

Edition Identifiers

Open Library: OL27186779M
Internet Archive: datasciencefroms0000grus
ISBN 10: 149190142X
ISBN 13: 9781491901427
LCCN: 2015472852
OCLC/WorldCat: 898161437, 907532468
Wikidata: Q106987804

Work Identifiers

Work ID: OL20006690W

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