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Data Science from Scratch: First Principles with Python
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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
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Kindle Edition, 330 pages
Published
April 14th 2015
by O'Reilly Media
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I'm still struggling to find the book I want around data science. I've learned that there are two levels:
1. KNOWING data science
2. DOING data science
This book is about the second one. Make no mistake, this is a "statistical computation" manual. This shows you how to find statistical answers using Python. Fully half this book is code samples. If you do not plan to actually attempt to find statistical answers to known questions by writing Python code, then this isn't the book for you.
I would look ...more
1. KNOWING data science
2. DOING data science
This book is about the second one. Make no mistake, this is a "statistical computation" manual. This shows you how to find statistical answers using Python. Fully half this book is code samples. If you do not plan to actually attempt to find statistical answers to known questions by writing Python code, then this isn't the book for you.
I would look ...more
I worked thru all of the examples in this book. Rather than have you import numpy and pandas and scikit-learn, he walks you through how to build up these tools yourself. What you build will be terribly inefficient and you should never use them in real life, but you will get a great feel for how they work under the hood.
(I also learned that my linear algebra is very rusty and I need a brush up ...)
I disagree with some of the reviews that they he doesn't do a good job explaining the computation ...more
(I also learned that my linear algebra is very rusty and I need a brush up ...)
I disagree with some of the reviews that they he doesn't do a good job explaining the computation ...more
The idea of the book is nice, I still think is a useful book, but:
1. you'll not learn math behind this or the methods will be explained (it's good for a programming, though)
2. regarding programming part, I think that people would benefit more if there were some actual exercises for them to do, not just "type in this code" attitude
3. would be nice if all of the data sets are actually generated in a book, not just "there is some data set with 2000 points, that I just pulled out of my ass"
4. more u ...more
1. you'll not learn math behind this or the methods will be explained (it's good for a programming, though)
2. regarding programming part, I think that people would benefit more if there were some actual exercises for them to do, not just "type in this code" attitude
3. would be nice if all of the data sets are actually generated in a book, not just "there is some data set with 2000 points, that I just pulled out of my ass"
4. more u ...more
As the title implies, this book will show you how to implement basic linear algebra, statistics, probability methods and ML models in pure Python.
+ The book covers all necessary basic topics for you to getting started with data science and also shows us where to dig in deeper in those topics.
+ Python with type hinting is a big plus. Some people may hate it but I think it's a good feature. In real life, it may depend on your team.
- Not enough mathematics explanations.
- This is too "scratchy". I w ...more
+ The book covers all necessary basic topics for you to getting started with data science and also shows us where to dig in deeper in those topics.
+ Python with type hinting is a big plus. Some people may hate it but I think it's a good feature. In real life, it may depend on your team.
- Not enough mathematics explanations.
- This is too "scratchy". I w ...more
Not terribly impressed with this one. The way I see it, readers of this book either will already know how to do data science, or they won't. If they do (and here I'm ignoring the fact that why would they, since the title of the book is "data science from scratch"), then they will find the explanations of concepts too basic, and the Python code implementation examples mostly useless (they, after all, are not using the libraries specifically designed to do data science, but rather implementing a n
...more
Great book for a general overview of the concepts, and understanding what 'data science' actually means. Lots of code to drive to the points home, and it taught me quite a few Python tricks.
I can foresee using this as a reference for the main concepts, or when looking for a straightforward implementation of the algorithms discussed. The information is very solid.
If you want to power straight through, it's a tough read at times--but Joel's a very good writer, and I enjoyed the dry humor intersp ...more
I can foresee using this as a reference for the main concepts, or when looking for a straightforward implementation of the algorithms discussed. The information is very solid.
If you want to power straight through, it's a tough read at times--but Joel's a very good writer, and I enjoyed the dry humor intersp ...more
Fundamental concepts revealed, libraries for the win
Joel does a great job walking through the tasks a data scientist would take to solve hypothetical problems, and explaining the models most popularly implemented in machine learning. An overwhelming majority of the code examples are useless, which is intentional as Joel notes how to build things from scratch. Libraries (like pandas, scikit-learn, etc) provide APIs to accomplish many of these tasks without writing from scratch, but without the un ...more
Joel does a great job walking through the tasks a data scientist would take to solve hypothetical problems, and explaining the models most popularly implemented in machine learning. An overwhelming majority of the code examples are useless, which is intentional as Joel notes how to build things from scratch. Libraries (like pandas, scikit-learn, etc) provide APIs to accomplish many of these tasks without writing from scratch, but without the un ...more
This was a fun survey of popular topics in contemporary data science. It was well written for a text book, and easy to read. I suppose it was light on formal proofs, but it made up for that by having you build toy models of all the major ideas. Well worth the read for me, as I am very new to data science but well versed in Python and math. I would like to see a follow-up book that covers the same topics, but using the real libraries people use in industry to solve these same problems.
more entertaining that an entry level programming language text would usually be, and not at the expense of content. well, maybe somewhat at the expense of content because some of the examples are a little too simple to give a real feel for what the methods are useful for. but overall lots of fun and very good information. i did find it a little frustrating, especially early on, that no equations were included and reading python was necessary to understand the fundamentals.
Quick read. And a great intro that brushes over the area of Data Science. Even though it does not convey much knowledge that could be used by a practicing Data Scientist. The "from Scratch" part of the title refers to the book's focus on the implementation of the popular algorithms using Python. From scratch. Which is... rather pointless. Anyone serious about Data Science would use pre-packaged, efficient libraries to train their models instead. The author does send the reader to external source
...more
Practical book which covers what's essential for data analysts getting into statistical analysis, machine learning and related topics. Good book for those starting out, but didn't have much to offer on the statistical learning side, principles and concepts wise. You're better off looking at books such as IPSUR (Jay G Kearns) and ISLR (Hastie & Tibshirani) for such content. However, this is a practical book because it introduces many relevant ideas. Some qualms: MapReduce treatment is probably ou
...more
Aside form the author's enthusiasm and breadth of knowledge I did not get much out of this book. For me there are not enough details on the statistical concepts and too much detail in the 'from scratch' code samples. The code samples are also never to be used again, as the author admits at the end, because there are many python packages that do an infinitely more efficient and scalable job of analysing data. The modelling concepts are not differentiated clearly enough so it's not understood why
...more
I read this prior to beginning an MSc in Data Science and found it to be a great introduction to data science, starting out with the very basics before moving into more general ML techniques and finishing up with some of the more complex topics such as MapReduce. Not an in-depth textbook by any means, but I do not think that is the purpose of this book, moreover to give the reader a well-rounded idea of the field.
It is a wonderful book to understand the detail of some machine learning methods implementation. It is also a good practice to use Python basic. As it is suggested, everything function is constructed from scratch. I really enjoyed the book, however I would not recommend it to learn ML and go directly to developing ML applications.
I rate it 4 because , some examples shown in the book do not provide data to test them
I rate it 4 because , some examples shown in the book do not provide data to test them
I have started this the second time now.
I really like the basic idea of doing things "from scratch", to get a better understanding, but I realize that it really requires you to run through pretty much every code example to follow intelligibly. Add to this that it starts fairly basic, I feel it is taking just a bit too much of my time to seem worth it. Considering dropping it. We'll see. Probably great for someone very new to python though.
I really like the basic idea of doing things "from scratch", to get a better understanding, but I realize that it really requires you to run through pretty much every code example to follow intelligibly. Add to this that it starts fairly basic, I feel it is taking just a bit too much of my time to seem worth it. Considering dropping it. We'll see. Probably great for someone very new to python though.
I've been reading this book off and on for over a year and have enjoyed it. As the book's title implies you're basically building up a library of tools for data science from scratch in python. As data science largely builds on statistics and linear algebra, the first part of the book mainly builds on those concepts. My only complaint is that you build up a library of tools, but little time is spent on how to use them.
The book covers a vast topic required to get started with data science stream. It introduces theory, frameworks and library. As a result none of the topics is hands on with example problem solving. Though the book working code example for all the concepts. To get a decent grip in data science the problem solving is very crucial.
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.
Even though the book is shallow, I would recommend it; here and there you can get a valuable piece of information from it.
Data Science from Scratch is a good Data Science overview. It covers the breadth of the "field" targeting (aspiring) practitioners (for example, I couldn't find a "definition" of data science beyond the "it's a Venn diagram thing - data, math, hacking"). For practitioners, the "from scratch" approach is very useful. Some topics will be o quick skim, others are a close analysis of the code (python) to understand specific implementation of "cartoon" examples. The from-scratch approach builds up th
...more
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“This means that, where appropriate, we will dive into mathematical equations, mathematical intuition, mathematical axioms, and cartoon versions of big mathematical ideas.”
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