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Data Science from Scratch: First Principles with Python #2020

Data Science from Scratch: First Principles with Python By Joel Grus Data Science from Scratch First Principles with Python 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
  • Title: Data Science from Scratch: First Principles with Python
  • Author: Joel Grus
  • ISBN: -
  • Page: 292
  • Format: Kindle Edition
  • Data Science from Scratch: First Principles with Python By Joel Grus 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 somData 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 PythonLearn the basics of linear algebra, statistics, and probability and understand how and when they re used in data scienceCollect, explore, clean, munge, and manipulate dataDive into the fundamentals of machine learningImplement models such as k nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clusteringExplore recommender systems, natural language processing, network analysis, MapReduce, and databases
    Data Science from Scratch: First Principles with Python By Joel Grus
    • [EPUB] ☆ Data Science from Scratch: First Principles with Python | by ↠ Joel Grus
      Joel Grus

    About "Joel Grus"

    1. Joel Grus

      Joel Grus Is a well-known author, some of his books are a fascination for readers like in the Data Science from Scratch: First Principles with Python book, this is one of the most wanted Joel Grus author readers around the world.

    295 Comments

    1. I m still struggling to find the book I want around data science I ve learned that there are two levels 1 KNOWING data science2 DOING data scienceThis 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 at the co [...]


    2. Decent book on introduction to data science using PythonW, we should seriously stop writing books on elementary data science using R or Python We have too many and they already started to look alike.


    3. 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 he does t [...]


    4. to be read for purposes of demonstrating fundamentals most of work here can be accomplished much simpler with advanced libraries, but this type of text helps one to understand the why and the building blocks of elaborate practice.


    5. Good introductory book on data science I would recommend this to people who wish to learn basic things in a hands on fashion.



    6. 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 if there were some actual exercises for them to do, not just type in this code attitude3 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 usage of numpy wo [...]


    7. 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 interspersed t [...]


    8. Fundamental concepts revealed, libraries for the winJoel 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 underl [...]


    9. De nada adianta conhecer ci ncia de dados sem fazer ci ncia de dados Partindo deste pressuposto, este livro traz o essencial para colocar a m o na massa e torturar alguns dados O mais interessante deste livro que ele parte do absoluto zero nos algoritmos Por n o confiar em nenhuma biblioteca de an lise, ele demonstra toda constru o t cnica por traz de regress es, redes neurais, rvores de decis o, classificadores bayesianos, etc.Leitura recomendada para um s lido entendimento da pr tica de Data S [...]


    10. 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.


    11. An excellent tool for aspiring data scientists like myself.There s no shortage of information on the topic, but it s hard to find it all in one place You could spend weeks combing through forums, blog posts, and video tutorials only to find half as much useful information Data Science from Scratch covers the foundations of many basic Machine Learning algorithms in a succinct and humorous way.As fair warning, the math is a little much to take in for a single book The author provides introductions [...]


    12. 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 outdated alre [...]


    13. 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 efficient and scalable job of analysing data The modelling concepts are not differentiated clearly enough so it s not understood why you d use [...]


    14. 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.


    15. 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 the tools to give good u [...]


    16. Nice book to give a feel of Data Science It explains the principles of data science by giving a very basic implementation in Python There is a good follow up suggestions at the end of each chapter.



    17. 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, a [...]


    18. What is a data scientist It has been dubbed the sexiest job of the 21st century because we all know data is sexy Josh Wills defines a data scientists as A person who is better at statistics than any software engineer and better at software engineering than any statistician Let s say a data scientist is one who does data science , a nebulous term which ranges over a wide number of disciplines such as machine learning, statistics, hacking etc.Joel Grus helps clear up this confusion with his warm a [...]


    19. This is a very basic into topics in statistics and machine learning built around functioning code to perform some of the tasks and algorithms discussed.As an introduction it seemed very solid I was looking for something a little in depth, so this was not really the book I was looking for What am I looking for Something that bridges between a working knowledge of e.g some methods in scikit learn to e.g coding those methods, from scratch Gradient descent and PCA are covered, but the book stops pr [...]


    20. Some nifty ideas and cool code, but ever since I fell head over heels into R and now, Jullia universe, I am just not that impressed with python as an analytical tool Sure, it is a wonderful glue and my favourite general scripting language around, but I find it easier and fun to call my R scripts from my python scripts, rather than implementing the whole shebang in python.That said, it is a good introductory text on the subject, subject to your coding preferences.


    21. 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.


    22. Understand the fundamentals in Python Really enjoyed working my way through this book It was an excellent refresher on core Data Science concepts, reinforced by building the equivalent of very basic data science libraries I greatly enjoyed Joel s writing style, examples, and introduced problems Highly recommended.


    23. It is a good overview book on Data Science I found it interesting that the author tried to tell a story throughout the book However, only 8 10 pages on MapReduce weren t enough Some of the examples might be done a bit better.


    24. A good introduction to data science using pure python and not using special libraries The codes in the book explain what is hidden under the hood One can also learn python essentials by reading this book.


    25. Introduce a wide variety of content about Data Science and provide a lot of pieces of code mainly functions It s the biggest advantage as well the biggest disadvantage.



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