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Data Science from Scratch

First Principles with Python

Paperback Engels 2019
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Samenvatting

To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.

Get a crash course in Python
- Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science
- Collect, explore, clean, munge, and manipulate data
- Dive into the fundamentals of machine learning
- Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Specificaties

ISBN13:9781492041139
Taal:Engels
Bindwijze:paperback
Aantal pagina's:385
Uitgever:O'Reilly
Druk:2
Verschijningsdatum:6-6-2019

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Over Joel Grus

Joel Grus is a software engineer at Google. Before that he worked as a data scientist at multiple startups. He lives in Seattle, where he regularly attends data science happy hours. He blogs infrequently at joelgrus.com.

Andere boeken door Joel Grus

Inhoudsopgave

Preface to the Second Edition
Conventions Used in This Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us
Acknowledgments
Preface to the First Edition
Data Science
From Scratch

1. Introduction
The Ascendance of Data
What Is Data Science?
Motivating Hypothetical: DataSciencester
Finding Key Connectors
Data Scientists You May Know
Salaries and Experience
Paid Accounts
Topics of Interest
Onward

2. A Crash Course in Python
The Zen of Python
Getting Python
Virtual Environments
Whitespace Formatting
Modules
Functions
Strings
Exceptions
Lists
Tuples
Dictionaries
defaultdict
Counters
Sets
Control Flow
Truthiness
Sorting
List Comprehensions
Automated Testing and assert
Object-Oriented Programming
Iterables and Generators
Randomness
Regular Expressions
Functional Programming
zip and Argument Unpacking
args and kwargs
Type Annotations
How to Write Type Annotations
Welcome to DataSciencester!
For Further Exploration

3. Visualizing Data
matplotlib
Bar Charts
Line Charts
Scatterplots
For Further Exploration

4. Linear Algebra
Vectors
Matrices
For Further Exploration

5. Statistics
Describing a Single Set of Data
Central Tendencies
Dispersion
Correlation
Simpson’s Paradox
Some Other Correlational Caveats
Correlation and Causation
For Further Exploration

6. Probability
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
p-Values
Confidence Intervals
p-Hacking
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
Using Gradient Descent to Fit Models
Minibatch and Stochastic Gradient Descent
For Further Exploration

9. Getting Data
stdin and stdout
Reading Files
The Basics of Text Files
Delimited Files
Scraping the Web
HTML and the Parsing Thereof
Example: Keeping Tabs on Congress
Using APIs
JSON and XML
Using an Unauthenticated API
Finding APIs
Example: Using the Twitter APIs
Getting Credentials
For Further Exploration

10. Working with Data
Exploring Your Data
Exploring One-Dimensional Data
Two Dimensions
Many Dimensions
Using NamedTuples
Dataclasses
Cleaning and Munging
Manipulating Data
Rescaling
An Aside: tqdm
Dimensionality Reduction
For Further Exploration

11. Machine Learning
Modeling
What Is Machine Learning?
Overfitting and Underfitting
Correctness
The Bias-Variance Tradeoff
Feature Extraction and Selection
For Further Exploration

12. k-Nearest Neighbors
The Model
Example: The Iris Dataset
The Curse of Dimensionality
For Further Exploration

13. Naive Bayes
A Really Dumb Spam Filter
A More Sophisticated Spam Filter
Implementation
Testing Our Model
Using Our Model
For Further Exploration

14. Simple Linear Regression
The Model
Using Gradient Descent
Maximum Likelihood Estimation
For Further Exploration

15. Multiple Regression
The Model
Further Assumptions of the Least Squares Model
Fitting the Model
Interpreting the Model
Goodness of Fit
Digression: The Bootstrap
Standard Errors of Regression Coefficients
Regularization
For Further Exploration

16. Logistic Regression
The Problem
The Logistic Function
Applying the Model
Goodness of Fit
Support Vector Machines
For Further Investigation

17. Decision Trees
What Is a Decision Tree?
Entropy
The Entropy of a Partition
Creating a Decision Tree
Putting It All Together
Random Forests
For Further Exploration

18. Neural Networks
Perceptrons
Feed-Forward Neural Networks
Backpropagation
Example: Fizz Buzz
For Further Exploration

19. Deep Learning
The Tensor
The Layer Abstraction
The Linear Layer
Neural Networks as a Sequence of Layers
Loss and Optimization
Example: XOR Revisited
Other Activation Functions
Example: FizzBuzz Revisited
Softmaxes and Cross-Entropy
Dropout
Example: MNIST
Saving and Loading Models
For Further Exploration

20. Clustering
The Idea
The Model
Example: Meetups
Choosing k
Example: Clustering Colors
Bottom-Up Hierarchical Clustering
For Further Exploration

21. Natural Language Processing
Word Clouds
n-Gram Language Models
Grammars
An Aside: Gibbs Sampling
Topic Modeling
Word Vectors
Recurrent Neural Networks
Example: Using a Character-Level RNN
For Further Exploration

22. Network Analysis
Betweenness Centrality
Eigenvector Centrality
Matrix Multiplication
Centrality
Directed Graphs and PageRank
For Further Exploration

23. Recommender Systems
Manual Curation
Recommending What’s Popular
User-Based Collaborative Filtering
Item-Based Collaborative Filtering
Matrix Factorization
For Further Exploration

24. Databases and SQL
CREATE TABLE and INSERT
UPDATE
DELETE
SELECT
GROUP BY
ORDER BY
JOIN
Subqueries
Indexes
Query Optimization
NoSQL
For Further Exploration

25. MapReduce
Example: Word Count
Why MapReduce?
MapReduce More Generally
Example: Analyzing Status Updates
Example: Matrix Multiplication
An Aside: Combiners
For Further Exploration

26. Data Ethics
What Is Data Ethics?
No, Really, What Is Data Ethics?
Should I Care About Data Ethics?
Building Bad Data Products
Trading Off Accuracy and Fairness
Collaboration
Interpretability
Recommendations
Biased Data
Data Protection
In Summary
For Further Exploration

27. Go Forth and Do Data Science
IPython
Mathematics
Not from Scratch
NumPy
pandas
scikit-learn
Visualization
R
Deep Learning
Find Data
Do Data Science
Hacker News
Fire Trucks
T-Shirts
Tweets on a Globe
And You?

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