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Data Science Live Book
It’s a book to learn data science, machine learning and data analysis with tons of examples and explanations around several topics
The book’s premise is “everything is related to everything” . It is noticed in the relationship across different sections, for example choosing the right data type for any variable could be related to dealing with missing values, and vice-versa.
In addition, some technical examples are related to “real-life” situations as well as philosophical concepts. The ultimate goal is to simplify the learning journey.
It's a playbook with full of data preparation receipts, using the open source R language.
There are two types of examples, some are oriented to teach general concepts around data analysis (like the information theory concept), while others are intended to show how to transform missing values, choosing the correct data type, and the implications in any case; among others, using easy copy-paste pieces of code.
Please note that this is not a book to learn how to program R from scratch, nor how algorithms are implemented (math and stats area).
It’s aimed to people who are Programmers and data scientists who work -or want to- in machine learning projects. However, the ones who don’t want -or don’t know- how to code, can get some useful insights which can add value as data project analysts.
All the R examples are well explained in code comments.
No math or statistical background to understand it.
The book tries to be as tool-independent as possible. For example, the decision of what to do to deal with missing or extreme values is the whether we choose R, Python, Julia. What it changes is the how.
To develop a critical thinking, without taking any statement as the "true truth" , it’s essential in this sea of books, courses, videos and any technical material to learn. This book is just another view in the data science perspective. Hope you like it :)
Index
Exploratory data analysis
Data preparation
Selecting best variables
Assesing Model performance
Appendix
The book’s premise is “everything is related to everything” . It is noticed in the relationship across different sections, for example choosing the right data type for any variable could be related to dealing with missing values, and vice-versa.
In addition, some technical examples are related to “real-life” situations as well as philosophical concepts. The ultimate goal is to simplify the learning journey.
It's a playbook with full of data preparation receipts, using the open source R language.
There are two types of examples, some are oriented to teach general concepts around data analysis (like the information theory concept), while others are intended to show how to transform missing values, choosing the correct data type, and the implications in any case; among others, using easy copy-paste pieces of code.
Please note that this is not a book to learn how to program R from scratch, nor how algorithms are implemented (math and stats area).
It’s aimed to people who are Programmers and data scientists who work -or want to- in machine learning projects. However, the ones who don’t want -or don’t know- how to code, can get some useful insights which can add value as data project analysts.
All the R examples are well explained in code comments.
No math or statistical background to understand it.
The book tries to be as tool-independent as possible. For example, the decision of what to do to deal with missing or extreme values is the whether we choose R, Python, Julia. What it changes is the how.
To develop a critical thinking, without taking any statement as the "true truth" , it’s essential in this sea of books, courses, videos and any technical material to learn. This book is just another view in the data science perspective. Hope you like it :)
Index
Exploratory data analysis
Data preparation
Selecting best variables
Assesing Model performance
Appendix
200 pages, Kindle Edition
Published October 29, 2017
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8 people want to read
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