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JavaScript for Data Science
JavaScript is the native language of the Internet. Originally created to make web pages more dynamic, it is now used for software projects of all kinds, including scientific visualization and data services. However, most data scientists have little or no experience with JavaScript, and most introductions to the language are written for people who want to build shopping carts rather than share maps of coral reefs.
This book will introduce you to JavaScript's power and idiosyncrasies and guide you through the key features of the language and its tools and libraries. The book places equal focus on client- and server-side programming, and shows readers how to create interactive web content, build and test data services, and visualize data in the browser. Topics
The core features of modern JavaScript
Creating templated web pages
Making those pages interactive using React
Data visualization using Vega-Lite
Using Data-Forge to wrangle tabular data
Building a data service with Express
Unit testing with Mocha
All of the material is covered by the Creative Commons Attribution-Noncommercial 4.0 International license (CC-BY-NC-4.0) and is included in the book's companion website.
.Maya Gans is a freelance data scientist and front-end developer by way of quantitative biology. Toby Hodges is a bioinformatician turned community coordinator who works at the European Molecular Biology Laboratory. Greg Wilson co-founded Software Carpentry, and is now part of the education team at RStudio
This book will introduce you to JavaScript's power and idiosyncrasies and guide you through the key features of the language and its tools and libraries. The book places equal focus on client- and server-side programming, and shows readers how to create interactive web content, build and test data services, and visualize data in the browser. Topics
The core features of modern JavaScript
Creating templated web pages
Making those pages interactive using React
Data visualization using Vega-Lite
Using Data-Forge to wrangle tabular data
Building a data service with Express
Unit testing with Mocha
All of the material is covered by the Creative Commons Attribution-Noncommercial 4.0 International license (CC-BY-NC-4.0) and is included in the book's companion website.
.Maya Gans is a freelance data scientist and front-end developer by way of quantitative biology. Toby Hodges is a bioinformatician turned community coordinator who works at the European Molecular Biology Laboratory. Greg Wilson co-founded Software Carpentry, and is now part of the education team at RStudio
234 pages, Kindle Edition
Published February 3, 2020
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Displaying 1 of 1 review
September 20, 2020
I had high hopes for this book but it was a huge disappointment. It certainly wasn't worth the A$80 I paid for it and I don't recommend it. It is brief, non-comprehensive, lacking in any unifying principles, and lacking any kind of "data science" framework.
I had been hoping for one of two things:
- an equivalent of R for Data Science or Python for Data Analysis, which takes you through analysing real world data sets, gets you making charts from day one and teaches how to do data munging and basic statistics in a hands-on way
- a deep dive into the most common uses of JavaScript for data scientists which in my observation is overwhelmingly to create complex data visualisations or data-intensive web apps.
The book is neither of these (by a long shot). We have to wait until chapter 8 before visualization is even introduced, solely in the form of vega-lite. And there is limited real world data until right at the end. Instead, the book is a light (and short) taster of things anyone building multitier web applications in JavaScript needs to know, with in my view very little data orientation at all. The approach is abstract, unrelated to realistic applications, and in my view likely to be of little use to the professed target audience.
A shame, because we badly need a good book on JavaScript for data scientists - but this isn't it.
I had been hoping for one of two things:
- an equivalent of R for Data Science or Python for Data Analysis, which takes you through analysing real world data sets, gets you making charts from day one and teaches how to do data munging and basic statistics in a hands-on way
- a deep dive into the most common uses of JavaScript for data scientists which in my observation is overwhelmingly to create complex data visualisations or data-intensive web apps.
The book is neither of these (by a long shot). We have to wait until chapter 8 before visualization is even introduced, solely in the form of vega-lite. And there is limited real world data until right at the end. Instead, the book is a light (and short) taster of things anyone building multitier web applications in JavaScript needs to know, with in my view very little data orientation at all. The approach is abstract, unrelated to realistic applications, and in my view likely to be of little use to the professed target audience.
A shame, because we badly need a good book on JavaScript for data scientists - but this isn't it.
Displaying 1 of 1 review