AI News, Revolutions

Revolutions

The book begins with the following clear definition of Data Science: Data Science refers to an emerging area of work concerned with the collection, preparation, analysis, visualization, management and preservation of large collections of information.

For R beginners a Getting Started with R chapter is included, but it does get into some fairly in-depth topics including sentiment analysis of Twitter data, working with data in Hadoop via RHadoop, and creating information maps.

R for Data Science

This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it.

Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides.

Data Science for Startups: Blog -> Book

There’s a number of compelling reasons for data scientists to write books.

I didn’t take this formal approach to peer review, but I did incorporate feedback when converting blog posts to book chapters.

I like using Medium for writing as opposed to other editors, because I don’t have to worry about formatting, there is a built-in spell checker, and it’s easy to include code snippets.

There are some features that are missing that would be useful when authoring a book, such as being able to reference figures, code blocks, or bibliography entries, but it does provide a good starting point.

For building the book I used the bookdown package, which is an R library that converts R markdown files to PDF, epub, and web formats for publishing a book.

KDP provides good tools for reviewing the content of the book interior, provides a useful cover designer, and provides a wide variety of book sizes.

In reality, I build MVPs of parts of a data platform and used publicly available data sets to reduce the amount of system building that I needed to do before authoring chapters.

To accomplish this, I copied the text from the post into a new file, added section and subsection headers, added code block identifiers, replaced hyperlinks with footnotes, and added images using the include_graphics function in knitr.

My recommendations when writing a technical book are to lock down tooling early on if possible, and to accolate much more time than you think will be necessary to write a book, especially if you’re going to be learning something new along the way.

Data Smart: Using Data Science to Transform Information into Insight 1st Edition

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Data Science Live Book

Although it is true that computing power is being increased exponentially, the machines rebellion is far from happening today.

But just in case, it’d be a good practice not to trust blindly in black-box solutions without knowing, for example, how the system picks up the best variables, what the inner procedure to validate the model is, how it deals with extremes or rare values, among other topics covered in this book.

If you are evaluating some machine learning platform, some issues stated in this book can help you to decide the best option.

(Original quote in Spanish: “La maquina la hace el hombre, y es lo que el hombre hace con ella.”) By Jorge Drexler (musician, actor and doctor).

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