JavaScript for Data Science: 1st Edition (Paperback) book cover

JavaScript for Data Science

1st Edition

By Maya Gans, Toby Hodges, Greg Wilson

Chapman and Hall/CRC

226 pages

Purchasing Options:$ = USD
Paperback: 9780367422486
pub: 2020-02-07
Available for pre-order. Item will ship after 7th February 2020
Hardback: 9780367426521
pub: 2020-02-07
Available for pre-order. Item will ship after 7th February 2020

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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 include:

  • 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 at .

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

About the Series

Chapman & Hall/CRC Data Science Series

Reflecting the interdisciplinary nature of the field, this new data science book series brings together researchers, practitioners, and instructors from statistics, computer science, machine learning, and analytics. The series will publish cutting-edge research, industry applications, and textbooks in data science.

* Presents the latest research and applications in the field, including new statistical and computational techniques
* Covers a broad range of interdisciplinary topics
* Provides guidance on the use of software for data science, including R, Python, and Julia
* Includes both introductory and advanced material for students and professionals
* Presents concepts while assuming minimal theoretical background

The scope of the series is broad, including titles in machine learning, pattern recognition, artificial intelligence, predictive analytics, business analytics, visualization, programming, software, learning analytics, data collection and wrangling, interactive graphics, reproducible research, and more. The inclusion of examples, applications, and code implementation is essential.

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Programming / Games
COMPUTERS / Computer Science
COMPUTERS / Data Processing
COMPUTERS / Programming Languages / General
COMPUTERS / Programming Languages / JavaScript
COMPUTERS / Web / Web Programming
COMPUTERS / Data Modeling & Design