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.
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Customers say
Customers find the book good at introducing data science foundations and appreciate its intelligent writing style. They describe it as easy to read and follow, with helpful code examples throughout. The book receives positive feedback for its data analysis content. Customers have mixed opinions about the example quality and concept development.
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67 customers mention introduction, 56 positive, 11 negative
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 bookRead more
18 customers mention writing style, 18 positive, 0 negative
Really enjoyed this book, so many examples, well written, I'm buying a paper copy for the library. Well worth the purchaseRead 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 positive, 2 negative
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 positive, 3 negative
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 positive, 2 negative
...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
9 customers mention example quality, 5 positive, 4 negative
...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 purchaseRead 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 positive, 3 negative
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
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- ahrarReviewed in India on September 19, 2020
Data Science from Scratch
Format: PaperbackVerified Purchase -
Cliente AmazonReviewed in Brazil on March 10, 2018
Interesting, but shallow
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CDevReviewed in Mexico on April 25, 2016
Un libro que merece la pena para todas las personas que quieran adentrarse al mundo de data science.
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JubiReviewed in France on July 21, 2015
Parfait pour débuter en Data Science
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MARTAReviewed in Spain on December 15, 2017
Muy buen libro de básicos del mundo DS para también iniciarse a Python
Format: PaperbackVerified Purchase
