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Author: Joel Grus

To truly learn data science, you must not only master the tools – data science libraries, frameworks, code units, and toolkits – but also understand the ideas and principles that underpin their...

To truly learn data science, you must not only master the tools – data science libraries, frameworks, code units, and toolkits – but also understand the ideas and principles that underpin their functioning.

This second edition of Data Science: Principles and Applications with Python, updated for Python 3.6, shows you how these tools and algorithms work by...

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Genre: Technology Computers - Informatics Statistics Artificial Intelligence
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Description

Description

To truly learn data science, you must not only master the tools – data science libraries, frameworks, code units, and toolkits – but also understand the ideas and principles that underpin their functioning.

This second edition of Data Science: Principles and Applications with Python, updated for Python 3.6, shows you how these tools and algorithms work by applying them from the very first steps. If you have a knack for mathematics and programming skills, author Joel Grus will help you feel comfortable with the mathematics and statistics at the core of data science, as well as the essential “hacking” knowledge needed to get started as data scientists.

With new material on deep learning, statistics, and natural language processing, this updated book shows you how to find hidden gems in today’s chaotic data landscape.

• Take a crash course in Python
• Learn the essential principles of linear algebra, statistics, and probability – and how and when they are used in data science
• Collect, explore, clean, transform, and process data
• Dive into the fundamentals of machine learning
• Implement models such as k-nearest neighbors, naive Bayes classification, linear and logistic regression, decision trees, neural networks, and clustering
• Explore recommendation systems, natural language processing, network analysis, MapReduce, and databases.

Book Reviews:
“Joel takes you from the elementary questions of data science to a complete understanding of the fundamental algorithms that every data scientist must know.” —Rohit Sivaprasad, Engineer, Facebook
“I recommend the book Data Science: Principles and Applications with Python to analysts and engineers who want to advance by mastering the field of machine learning. It is the best tool for understanding the essential principles of this scientific field.” —Tom Marthaler, Director of Engineering, Amazon
“It’s hard to turn data science concepts into code. Joel's book makes it much easier.” —William Cox, Machine Learning Engineer, Grubhub.

Joel Grus is a research engineer at the Allen Artificial Intelligence Institute. He previously worked as a software engineer at Google and as a data scientist at various startups. He lives in Seattle, where he regularly “attends” data science classes. He occasionally writes on his blog and tweets all day at @joelgrus.

Manufacturer

Manufacturer Details

Warnings & Safety Information
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Specifications

Specifications

Author
Joel Grus
Publisher
Papasotiriou
Original Title
Data Science from Scratch
Type
Technology, Computers - Informatics, Statistics, Artificial Intelligence
Language
Greek
Cover
Soft
Number of Pages
408
Release Date
11/2021
Publication Date
2021
Dimensions
17x24 cm
ISBN-13
9789604911448

Important information

Specifications are collected from official manufacturer websites. Please verify the specifications before proceeding with your final purchase. If you notice any problem you can report it here.

See all specifications

Reviews (3)

Reviews

4.7
3
4.7
Review this product
  1. 2
  2. 1
  3. 3 stars
    0
  4. 2 stars
    0
  5. 1 star
    0
Review this product
  • Was it easy to read?
  • Understanding of the subject matter
  • Was it interesting enough?
  • I liked the writing style
  • I would recommend it for reading
  • Paper quality
  • I would read a book by the same author
  • petridispa
    4
    17 out of 17 members found this review helpful
    petridispa

    The book is aimed at those who truly want to professionally engage with Python applications in data science and all its subsets (Machine Learning, Deep Learning, etc). It is partly good that it does not use the libraries scipy, scikit-learn, and tensorflow. Instead, it implements simple statistical metrics and relatively complex classifier models (Naive Bayes) in detail. This is the best approach for someone who has no prior experience in data analysis, as it allows them to see the basic mathematical tools that play a role. The book assumes basic knowledge of Python, and anyone who understands, understands. It comments on each method and occasionally provides advice. It might overwhelm you a bit with the frequent use of functions, but in the end, it will prove useful as you see how to write reusable code. Additionally, you will never be asked to write knn, k-means, or naive bayes from scratch like Grus does. Once you finish this book, you will be able to start exploring the scipy, scikit-learn, and tensorflow libraries and call each model with just one command. You may ask why then should you read this book that does everything from scratch? I would say that a data scientist should be a good mathematician-statistician, in addition to being a competent programmer. Therefore, by understanding the mathematics behind classification, clustering, and regression algorithms, you will gain an understanding of each algorithm's concept, which method is best suited for specific problems, and of course, how to optimize each method. Personally, I would have liked more comments and advice targeted at working with data (preprocessing, filling empty values) and dense clustering algorithms. In conclusion, if you partly agree with what I'm saying, take it and you don't need to read all the chapters right away. I recommend chapters 1 to 18 and 20.

    Translated from Greek ·
    • Was it easy to read?
    • Understanding of the subject matter
    • Was it interesting enough?
    • I liked the writing style
    • I would recommend it for reading
    • Paper quality
    • I might read a book by the same author
    Did you find this review helpful?
  • 5
    Kostikas_Pontiac

    Verified purchase

    • Paper quality
    • Was it easy to read?
    • Understanding of the subject matter
    • Was it interesting enough?
    • I liked the writing style
    • I would read a book by the same author
    • I would recommend it for reading
  • 5
    sgous

    Verified purchase

    • Paper quality
    • Was it easy to read?
    • Understanding of the subject matter
    • Was it interesting enough?
    • I liked the writing style
    • I would read a book by the same author
    • I would recommend it for reading
  • petridispa
    4
    petridispa

    The book is aimed at those who truly want to professionally engage with Python applications in data science and all its subsets (Machine Learning, Deep Learning, etc). It is partly good that it does not use the libraries scipy, scikit-learn, and tensorflow. Instead, it implements simple statistical metrics and relatively complex classifier models (Naive Bayes) in detail. This is the best approach for someone who has no prior experience in data analysis, as it allows them to see the basic mathematical tools that play a role. The book assumes basic knowledge of Python, and anyone who understands, understands. It comments on each method and occasionally provides advice. It might overwhelm you a bit with the frequent use of functions, but in the end, it will prove useful as you see how to write reusable code. Additionally, you will never be asked to write knn, k-means, or naive bayes from scratch like Grus does. Once you finish this book, you will be able to start exploring the scipy, scikit-learn, and tensorflow libraries and call each model with just one command. You may ask why then should you read this book that does everything from scratch? I would say that a data scientist should be a good mathematician-statistician, in addition to being a competent programmer. Therefore, by understanding the mathematics behind classification, clustering, and regression algorithms, you will gain an understanding of each algorithm's concept, which method is best suited for specific problems, and of course, how to optimize each method. Personally, I would have liked more comments and advice targeted at working with data (preprocessing, filling empty values) and dense clustering algorithms. In conclusion, if you partly agree with what I'm saying, take it and you don't need to read all the chapters right away. I recommend chapters 1 to 18 and 20.

    Translated from Greek ·
    17
  • 5
    Kostikas_Pontiac

    0
  • 5
    sgous

    0
  • See all

Description & Specifications

To truly learn data science, you must not only master the tools – data science libraries, frameworks, code units, and toolkits – but also understand the ideas and principles that underpin their functioning.

This second edition of Data Science: Principles and Applications with Python, updated for Python 3.6, shows you how these tools and algorithms work by applying them from the very first steps. If you have a knack for mathematics and programming skills, author Joel Grus will help you feel comfortable with the mathematics and statistics at the core of data science, as well as the essential “hacking” knowledge needed to get started as data scientists.

With new material on deep learning, statistics, and natural language processing, this updated book shows you how to find hidden gems in today’s chaotic data landscape.

• Take a crash course in Python
• Learn the essential principles of linear algebra, statistics, and probability – and how and when they are used in data science
• Collect, explore, clean, transform, and process data
• Dive into the fundamentals of machine learning
• Implement models such as k-nearest neighbors, naive Bayes classification, linear and logistic regression, decision trees, neural networks, and clustering
• Explore recommendation systems, natural language processing, network analysis, MapReduce, and databases.

Book Reviews:
“Joel takes you from the elementary questions of data science to a complete understanding of the fundamental algorithms that every data scientist must know.” —Rohit Sivaprasad, Engineer, Facebook
“I recommend the book Data Science: Principles and Applications with Python to analysts and engineers who want to advance by mastering the field of machine learning. It is the best tool for understanding the essential principles of this scientific field.” —Tom Marthaler, Director of Engineering, Amazon
“It’s hard to turn data science concepts into code. Joel's book makes it much easier.” —William Cox, Machine Learning Engineer, Grubhub.

Joel Grus is a research engineer at the Allen Artificial Intelligence Institute. He previously worked as a software engineer at Google and as a data scientist at various startups. He lives in Seattle, where he regularly “attends” data science classes. He occasionally writes on his blog and tweets all day at @joelgrus.

Manufacturer

Manufacturer Details

Warnings & Safety Information
Author
Joel Grus
Publisher
Papasotiriou
Original Title
Data Science from Scratch
Type
Technology, Computers - Informatics, Statistics, Artificial Intelligence
Language
Greek
Cover
Soft
Number of Pages
408
Release Date
11/2021
Publication Date
2021
Dimensions
17x24 cm
ISBN-13
9789604911448

Important information

Specifications are collected from official manufacturer websites. Please verify the specifications before proceeding with your final purchase. If you notice any problem you can report it here.

Reviews (3)

4.7
3
4.7
Review this product
  1. 2
  2. 1
  3. 3 stars
    0
  4. 2 stars
    0
  5. 1 star
    0
Review this product
  • Was it easy to read?
  • Understanding of the subject matter
  • Was it interesting enough?
  • I liked the writing style
  • I would recommend it for reading
  • Paper quality
  • I would read a book by the same author
  • petridispa
    4
    17 out of 17 members found this review helpful
    petridispa

    The book is aimed at those who truly want to professionally engage with Python applications in data science and all its subsets (Machine Learning, Deep Learning, etc). It is partly good that it does not use the libraries scipy, scikit-learn, and tensorflow. Instead, it implements simple statistical metrics and relatively complex classifier models (Naive Bayes) in detail. This is the best approach for someone who has no prior experience in data analysis, as it allows them to see the basic mathematical tools that play a role. The book assumes basic knowledge of Python, and anyone who understands, understands. It comments on each method and occasionally provides advice. It might overwhelm you a bit with the frequent use of functions, but in the end, it will prove useful as you see how to write reusable code. Additionally, you will never be asked to write knn, k-means, or naive bayes from scratch like Grus does. Once you finish this book, you will be able to start exploring the scipy, scikit-learn, and tensorflow libraries and call each model with just one command. You may ask why then should you read this book that does everything from scratch? I would say that a data scientist should be a good mathematician-statistician, in addition to being a competent programmer. Therefore, by understanding the mathematics behind classification, clustering, and regression algorithms, you will gain an understanding of each algorithm's concept, which method is best suited for specific problems, and of course, how to optimize each method. Personally, I would have liked more comments and advice targeted at working with data (preprocessing, filling empty values) and dense clustering algorithms. In conclusion, if you partly agree with what I'm saying, take it and you don't need to read all the chapters right away. I recommend chapters 1 to 18 and 20.

    Translated from Greek ·
    • Was it easy to read?
    • Understanding of the subject matter
    • Was it interesting enough?
    • I liked the writing style
    • I would recommend it for reading
    • Paper quality
    • I might read a book by the same author
    Did you find this review helpful?
  • 5
    Kostikas_Pontiac

    Verified purchase

    • Paper quality
    • Was it easy to read?
    • Understanding of the subject matter
    • Was it interesting enough?
    • I liked the writing style
    • I would read a book by the same author
    • I would recommend it for reading
  • 5
    sgous

    Verified purchase

    • Paper quality
    • Was it easy to read?
    • Understanding of the subject matter
    • Was it interesting enough?
    • I liked the writing style
    • I would read a book by the same author
    • I would recommend it for reading
  • petridispa
    4
    petridispa

    The book is aimed at those who truly want to professionally engage with Python applications in data science and all its subsets (Machine Learning, Deep Learning, etc). It is partly good that it does not use the libraries scipy, scikit-learn, and tensorflow. Instead, it implements simple statistical metrics and relatively complex classifier models (Naive Bayes) in detail. This is the best approach for someone who has no prior experience in data analysis, as it allows them to see the basic mathematical tools that play a role. The book assumes basic knowledge of Python, and anyone who understands, understands. It comments on each method and occasionally provides advice. It might overwhelm you a bit with the frequent use of functions, but in the end, it will prove useful as you see how to write reusable code. Additionally, you will never be asked to write knn, k-means, or naive bayes from scratch like Grus does. Once you finish this book, you will be able to start exploring the scipy, scikit-learn, and tensorflow libraries and call each model with just one command. You may ask why then should you read this book that does everything from scratch? I would say that a data scientist should be a good mathematician-statistician, in addition to being a competent programmer. Therefore, by understanding the mathematics behind classification, clustering, and regression algorithms, you will gain an understanding of each algorithm's concept, which method is best suited for specific problems, and of course, how to optimize each method. Personally, I would have liked more comments and advice targeted at working with data (preprocessing, filling empty values) and dense clustering algorithms. In conclusion, if you partly agree with what I'm saying, take it and you don't need to read all the chapters right away. I recommend chapters 1 to 18 and 20.

    Translated from Greek ·
    17
  • 5
    Kostikas_Pontiac

    0
  • 5
    sgous

    0
  • See all
24,75 €
14,00 €   shipping cost