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Data Science from Scratch: First Principles with Python 1st Edition

by Joel Grus (Author)
4.4 out of 5 stars (405)
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Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science 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 hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.

  • Get a crash course in Python
  • Learn the basics of linear algebra, statistics, and probability―and understand 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, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
  • Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

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  1. ISBN-10
    149190142X
  2. ISBN-13
    978-1491901427
  3. Edition
    1st
  4. Publisher
    O'Reilly Media
  5. Publication date
    May 26, 2015
  6. Language
    English
  7. Dimensions
    7 x 0.75 x 9.25 inches
  8. Print length
    330 pages
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Editorial Reviews

About the Author

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.

Product details

  • Publisher ‏ : ‎ O'Reilly Media
  • Publication date ‏ : ‎ May 26, 2015
  • Edition ‏ : ‎ 1st
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 330 pages
  • ISBN-10 ‏ : ‎ 149190142X
  • ISBN-13 ‏ : ‎ 978-1491901427
  • Item Weight ‏ : ‎ 1.18 pounds
  • Dimensions ‏ : ‎ 7 x 0.75 x 9.25 inches
  • Best Sellers Rank: #519,843 in Books (See Top 100 in Books)
  • Customer Reviews:
    4.4 out of 5 stars (405)

About the author

Follow authors to get new release updates, plus improved recommendations.
Joel Grus

Joel Grus

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Joel Grus is Principal Engineer at Capital Group, where he leads a small team that designs and implements machine learning and data products. Before that he was a software engineer at the Allen Institute for AI and Google, and a data scientist at a variety of startups.

He's the author of the the beloved "Data Science from Scratch", the quirky "Ten Essays on Fizz Buzz", and the polarizing JupyterCon talk "I Don't Like Notebooks".

He lives in Seattle, where he regularly attends data science happy hours. He blogs infrequently at joelgrus.com.

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Customer reviews

4.4 out of 5 stars
405 global ratings

Customers say

Customers find the book good at introducing data science foundations and appreciate its intelligent, concise writing style. Moreover, the book is easy to read and follow, with code examples that can be executed along with the text. They value the data analysis content, with one customer highlighting its coverage of data modeling. Additionally, customers enjoy the book's humor. However, the concept development receives mixed feedback, with several customers noting that basic ideas are not presented well.

Select to learn more

IntroductionWriting styleEase of readingCode examplesData analysisHumorExample qualityConcept development
67 customers mention "Introduction"56 positive11 negative

Customers find the book provides a good introduction to data science concepts, with one customer noting it serves as a great reference for applying Python programming to database exploration.

"Good introduction to "what's out there" in data science and machine learning...." Read more

"Good introduction to data analysis. The clear syntax grammar of Python helps a lot to clarify the meaning of author." Read more

"This book is very practical and helpful...." Read more

"concise and good coverage, good data science introduction and reference book" Read more

18 customers mention "Writing style"18 positive0 negative

Customers appreciate the writing style of the book, finding it intelligently and concisely written, with one customer noting the author's ability to write elegant Python code.

"Really enjoyed this book, so many examples, well written, I'm buying a paper copy for the library. Well worth the purchase" Read more

"+1 for concise and to the point. +1 for relevant topics. -1 for lack of real life library usage. -1 for lack of real world data...." Read more

"...Other than these minor thoughts, I think this is an excellently written book and I highly recommend for anyone interested in data science, data..." Read more

"Pretty good intro to the topic, and covers a decent bit. Cleanly written, but a bit abbreviated in some parts...." Read more

14 customers mention "Ease of reading"12 positive2 negative

Customers find the book easy to read and follow, with one customer noting that each chapter can be read independently.

"Easy to follow, gives the reader a good intuition for what the algorithms are all about." Read more

"...+ well written I found the author funny, clear, and thorough - wish it dove deeper into libraries -..." Read more

"...basic concepts, math, and algorithms in data science with easy explanation...." Read more

"Easy to read and follow Well written, and explained !" Read more

12 customers mention "Code examples"9 positive3 negative

Customers appreciate the code examples in the book, with several mentioning the ability to code along with the examples. One customer notes that the code is surprisingly concise, while another points out the inclusion of Python pseudo code.

"Lots of coding examples for basic simple foundations to jump start your own projects." Read more

"This book is very practical and helpful. It contains the python pseudo code for many basic data science modeling methods, which was exactly what I..." Read more

"...the basic ideas were not presented so well which makes it hard to follow the code which write in great details...." Read more

"...It has a heavy focused on application with a lot of code examples, but sometimes I was missing the theory behind the example...." Read more

9 customers mention "Data analysis"7 positive2 negative

Customers appreciate the book's approach to data analysis, with one customer highlighting its coverage of data modeling and another noting its focus on algorithms in data science.

"...book and I highly recommend for anyone interested in data science, data analysis, or just wanting the skills to visualize clean and meaningful data...." Read more

"...a good job introducing you to the foundations of data science: data modeling, regression, classifying, clustering, NLP, Map Reduce, etc...." Read more

"...14 and 15 treat linear regression and multiple regression, fitting a model to data, and regularization to limit the tendency to overfit...." Read more

"...-1 for lack of real life library usage. -1 for lack of real world data. -1 NaN ^ 2/5 stars. I don't normally leave reviews...." Read more

5 customers mention "Humor"5 positive0 negative

Customers enjoy the book's humor.

"The book is hilarious and engaging...." Read more

"...the right size and correct coverage for the content and the author's sense of humor (indeed, that of a data scientist) resonates with the..." Read more

"Fun to read, and a great intro to the subject!" Read more

"...+ well written I found the author funny, clear, and thorough - wish it dove deeper into libraries -..." Read more

9 customers mention "Example quality"5 positive4 negative

Customers have mixed opinions about the examples in the book, with some appreciating them while others find them lacking.

"...It is filled with techniques, examples and illustrations of how to do practical procedures using python...." Read more

"...For being about "data" they all have pretty useless and unrealistic examples. This book is no exception...." Read more

"Really enjoyed this book, so many examples, well written, I'm buying a paper copy for the library. Well worth the purchase" Read more

"...No end to end example and instructions to do hands on. Did not keep up with title which says "from scratch" . Very disappointed." Read more

6 customers mention "Concept development"3 positive3 negative

Customers have mixed opinions about the book's concept development, with several noting that the basic ideas were not presented well.

"Very good book. The examples are diverse and well thought out...." Read more

"...In this book, the basic ideas were not presented so well which makes it hard to follow the code which write in great details...." Read more

"...about this book is the very clever/clean (and challenging) way of constructing code and solving problems. Things can get very ....nested...." Read more

"...Having said that, the ideas are presented in a somewhat unstructured way...." Read more

Top reviews from the United States

  • Dr. Howard B. Bandy
    5.0 out of 5 stars A clear explanation of some of the concepts central to data science
    Reviewed in the United States on February 5, 2016
    Format: PaperbackVerified Purchase
    The book begins with the basics of the Python language in a chapter entitled "A Crash Course in Python." Grus recommends the Anaconda distribution of Python 2.7, as do I. It is free, includes Python, NumPy, SciPy, matplotlib, and IPython that are used in the book, and includes pandas which we will use to handle financial data. This is not the book I would recommend for a person new to Python to learn the language, but it establishes the style and notation used for the remainder of the book.

    Chapters 4, 5, 6 are quick reviews of linear algebra and the Python data structures used, frequentist statistics, and probability, respectively. Chapter 7 discusses hypothesis and inference, and has a nice discussion of the beta distribution and its use in describing the "prior" distribution for Bayesian analysis.

    Chapter 8 begins to get into the data science with a description of the gradient descent method of finding the set of parameter values that maximize (or minimize) the objective function. The "from scratch" approach shows all the details.

    Chapter 10, Working with Data, begins with methods for exploring the data. Examining the distribution, plotting single dimensional data, comparing multiple data series, normalizing, rescaling, and dimensionality reduction.

    Chapter 11 begins machine learning -- models, overfitting, underfitting, bias-variance tradeoff, and feature extraction.

    Chapter 12 continues with k-nearest neighbors and the curse of dimensionality.

    Chapter 13 illustrates naive Bayes to implement a spam filter.

    Chapters 14 and 15 treat linear regression and multiple regression, fitting a model to data, and regularization to limit the tendency to overfit.

    Chapter 16 explains the logistic function and logistic regression. Examples look at measures of goodness of fit. The concept of support vector machine is explained, although the mathematics are beyond from scratch.

    Chapter 17 has a nice explanation of decision trees (the models that result from rule-based trading system development, such as AmiBroker). Entropy, as it applies to information content, is well explained and used to partition data as the rules are created. Random forests, one of the ensemble techniques for machine learning, is described in surprisingly concise code.

    Neural networks are described in chapter 18, including code for a feed forward, back propagation network that identifies digits. The interpretation of the weights of each of nodes gives insight into the workings of neural networks.

    The book continues on with discussions of clustering, natural language processing, network analysis, recommender system, and databases.

    While this is not the best book to learn Python, machine learning, or model development, it is valuable in explaining each of these topics with fully disclosed logic and computer code.

    This book gets five stars based on meeting its objectives -- to clearly illustrate some of the central concepts of data science.
    15 people found this helpful
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  • Kelly Maurice
    5.0 out of 5 stars Outstanding Book Providing Exactly What The Title Says
    Reviewed in the United States on June 9, 2016
    Format: PaperbackVerified Purchase
    I thoroughly enjoyed this book, one of my favorite books ever on programming. It does three things superbly: covers the basic low level tools of a data scientist (the "from scratch" part), gives a great overview of useful Python programming examples for those new to Python, and gives an amazingly succinct yet high level overview of the mathematics and statistics required for data science.

    At first I was very worried about this book based on the first few chapters for the one reason that the author was cracking jokes throughout the text and I thought if it kept up for the rest of the book I was going to be very upset. But it did not happen and it turns out to have been a very reasonable way to ease into this complicated subject.

    The author steps through the toolbox of the data scientist, chapter by chapter, giving useful, insightful, clear pieces of code and textual explanations of each topic. So, for those new to data science it gives just enough to get the basic idea of a concept in terms of code and mathematical explanation, and then moves on to the next topic.

    It is often said that in writing, less is better and this book gets things down to their essence. That is one of the great things about the book - that the length of each chapter is about 20 pages (over 25 chapters). So each chapter can be read and the code even exercised in about an hour. Further, the references at the end of each chapter invite the reader to expanded information at the level of one or more entire textbooks or references. Thus the book can be seen as kind of boiling down a 25-volume set of highly technical subject matter into roughly 300 pages.

    The topics that were explored the best seem to be the ones on probability, working with data, regression, clustering, and databases (SQL). Some of the small but dense code samples were tough to follow but that is based on their algorithmic complexity - such as that for logistical regression and MapReduce. Occasionally the author uses a term that is not defined or in the index (such as data munging - which I still haven't looked up to see what it means). There are only a small number of typos which indicates good editing. While the Python crash course was pretty good, Python is a vast language and there could have been more to that section.

    I read this book from cover to cover and stepped through logically all the code (but did not actually run any of it) and I would wholeheartedly recommend this book for anyone wanting to work in the area of data science or its related fields, such as big data engineering or data analysis.
    21 people found this helpful
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  • Unknown
    4.0 out of 5 stars Jump Start for understanding Data Science.
    Reviewed in the United States on December 22, 2016
    Format: PaperbackVerified Purchase
    Minus one star for using outdated Python 2.7. Essentially ALL data science tools you are likely to run across have been updated to Python 3.4+. I would have knocked off two stars but this book is actually quite good and delivers on its title.

    This is a very basic book on Data Science but it gives a broad overview which helps you get a perspective on the tools that are available. This book teaches methods by developing actual code for these methods. You will find in work situations that you will use library functions instead of "rolling your own" but this book helps bring the details together by having you actually code these techniques. I support this approach 100% Once you have this overview, you can drill down into specifics with other materials like textbooks or cookbooks.

    I'd did flinch at some of the explanations in this book but it really is a "from Scratch" approach and some things are simplified to avoid distractions.

    This book also teaches basic Python 2.7 with a quick start chapter, so it is self contained for any scientist or engineer that wants to get started adding Data Science techniques to their repertoire.
    13 people found this helpful
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  • jc
    5.0 out of 5 stars Good price, and quick delivery. Product as advertised.
    Reviewed in the United States on February 26, 2025
    Format: PaperbackVerified Purchase
    Good price, and quick delivery. Product as advertised.
    Helpful
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Top reviews from other countries

Translate all reviews to English
  • ahrar
    5.0 out of 5 stars Data Science from Scratch
    Reviewed in India on September 19, 2020
    Format: PaperbackVerified Purchase
    Data Science from scratch is must for the beginners who want an overview and theoretical concepts on python, data visualization, data science , ML ,neural networks and so on.
    It also has a crash course on Python
    Combining this with Hands on Machine Learning and Tensorflow it is a combination worth spending.
    Report
  • Cliente Amazon
    3.0 out of 5 stars Interesting, but shallow
    Reviewed in Brazil on March 10, 2018
    Format: PaperbackVerified Purchase
    This book is nice to improve the understanding of some details underlying the data science algorithms, but it falls short in the deepness of the content. Some concepts feels rushed and incomplete; the explanation sometimes isn't clear.

    Even though the book is shallow, I would recommend it; here and there you can get a valuable piece of information from it.
    Report
  • CDev
    5.0 out of 5 stars Un libro que merece la pena para todas las personas que quieran adentrarse al mundo de data science.
    Reviewed in Mexico on April 25, 2016
    Format: PaperbackVerified Purchase
    Un libro que merece la pena para todas las personas que quieran adentrarse al mundo de data science, y no tengan un punto de partida, este libro nos ayuda a identificar casos sencillos hasta casos complejos con una ayuda visual del problema.
    Report
  • Jubi
    5.0 out of 5 stars Parfait pour débuter en Data Science
    Reviewed in France on July 21, 2015
    Format: PaperbackVerified Purchase
    J'ai choisi ce livre car bien que ma formation universitaire ait contenu 30% de maths, je n'ai pas eu l'occasion de pratiquer beaucoup depuis. Le livre ne nécessite aucun pré-requis en maths à part les opérations basiques, et explique tous les concepts (statistiques, probabilités, algèbre linéaire, etc...) de manière très didactique.

    Je développe déjà en Python, donc la partie "crash course" m'a surtout servi à comprendre quelle partie de Python est utilisée pour la data science. Je pense que le livre peut etre difficile à appréhender si on n'a pas déjà des bases en programmation.

    Je recommande ce livre car il explique bien les bases, est plutôt drôle à lire, et contient des pointeurs vers d'autres ressources pour approfondir les sujets.
    Report
  • MARTA
    5.0 out of 5 stars Muy buen libro de básicos del mundo DS para también iniciarse a Python
    Reviewed in Spain on December 15, 2017
    Format: PaperbackVerified Purchase
    Un libro muy útil para iniciarse al mundo DS con Python, aunque creo que es recomendable tener un mínimo de conocimientos previos (ya sea en R o haber hecho algún curso online), si no puede ser que no sea tan básico.
    Report