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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�

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Book ID Asin: 149190142X
Book Title: Data Science from Scratch: First Principles with Python
Book Author: Joel Grus
Book Format: unknownBook Price: unknown
Book Category: Books, Computers & Technology, Programming and unknown
Book Rating: 302 ratings

Data Science from Scratch: First Principles with Python by Joel Grus Book Review

Name: Umair
Rating: 2.0 out of 5 stars
Title: It says alot, but teaches little.
Date: Reviewed in the United States on March 6, 2019
Review: +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.
-1 NaN
^ 2/5 stars.

I don't normally leave reviews. I buy books, read them, and put them away. I decided to make an exception for this one.

Before I go any further I'll give the benefit of the doubt that perhaps I had a misunderstanding of what this book was about. I am a data scientist who works in cyber security. I have been through a good amount of data science boot camps, books, and online training. They all have one thing in common. For being about "data" they all have pretty useless and unrealistic examples. This book is no exception. (I laughed at a part where the author literally says something similar to "real life data is usually very messy" and continues to not use real data).

I will start out with the good parts about this book. It gives you a good crash course into Python and pretty much every critical data science concept. It is concise and filled with code examples written from scratch with little to no libraries being used (which is also a bad thing, I will explain shortly). The flow of the book is well designed as well.

Now for the bad.
1. Pure Python is great, but should be kept at a minimum. Sure, it gives you a good understanding of how to implement a concept in pure Python, but that is not the industry standard whatsoever. There should have been a healthy amount of real world implementations to offset the typical college classroom feel to the book. It also tends to add too much filler to the content. It would have been much better if it was "read resource X to see how to implement this in pure Python" and not "read book X to see how to use a real world library to do this."

2. The data in this book is like all the data everyone uses in their examples. Completely useless. Randomly generated numbers, endless usage of the "coin flip" probability examples, typical artificial data that, I promise you, nobody analyzes on a daily basis. The book starts off with you role playing as a new data scientist for a fictional social networking platform for Data Scientists. Which was very promising start and I was eager to see how this "character" would deal with the data problems they would face... Spoiler, barely ever spoke about it. Most examples are riddled with typical Statistic 101 and randomly generated data. Yet again, another disappointment on that end.

Maybe I misunderstood what this book was about. I could be wrong. That being said, I am now afraid to touch another "Data Science" book or online resource because I am sufficiently tired of reading about 300 ways to solve a problem with np.random generated arrays then turning to my screen with real world data and literally looking like the Persian Room Cat Guardian.

I am not sure if I recommend this or not. It is a good book in the sense that you learn about what Data Science "contains" but definitely not how it applies to the real world.

Name: SifMan79
Rating: 5.0 out of 5 stars
Title: Great Pyrh
Date: Reviewed in the United States on March 12, 2021
Review: Easy to read and follow

Well written, and explained !

Name: happye
Rating: 5.0 out of 5 stars
Title: This book is very practical and helpful. It contains ...
Date: Reviewed in the United States on July 18, 2018
Review: 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 was looking for. I really hope there is more of such kind of book out there!

Name: Public name
Rating: 5.0 out of 5 stars
Title: Highly recommended for every non-statistician that wants to be a Data Scientist
Date: Reviewed in the United States on February 24, 2016
Review: In my view, too many people want to be data scientist and use advance statistical techniques that they don't fully understand. I believe Grus closes this gap with this fun introductory book to some of the basic techniques in data analysis. If you don't come from a statistical background (MS/PhD), then you should start with this book and move on to other texts such as Pattern Recognition and Neural Networks by Brian D. Ripley for a thorough review of classification methods, or the more popular and modern The Elements of Statistical Learning By Hastie, Tibshirani and Friedman.

Name: Kevin Preston
Rating: 5.0 out of 5 stars
Title: Good, Practical examples
Date: Reviewed in the United States on August 30, 2018
Review: Lots of coding examples for basic simple foundations to jump start your own projects.

Name: Abdelrahman Ali Hasan
Rating: 5.0 out of 5 stars
Title: Good price
Date: Reviewed in the United States on May 3, 2019
Review: Good price

Name: Herbert
Rating: 5.0 out of 5 stars
Title: This is a great reference for applying beginning python programming to database exploration
Date: Reviewed in the United States on October 17, 2015
Review: If you want to learn the basics of Data Science using Python, this book is a must have. This is a great reference for applying beginning python programming to database exploration. It is filled with techniques, examples and illustrations of how to do practical procedures using python. A great trip "Down the Rabbit Hole." You will not be disappointed.

Name: Kelly Maurice
Rating: 5.0 out of 5 stars
Title: Outstanding Book Providing Exactly What The Title Says
Date: Reviewed in the United States on June 9, 2016
Review: 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.