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Key benefits

Build microservices to perform data transformation and ML model serving in JavaScript

Explore what Danfo.js is and how it helps with data analysis and data visualization

Combine Danfo.js and TensorFlow.js for machine learning

Description

Most data analysts use Python and pandas for data processing for the convenience and performance these libraries provide. However, JavaScript developers have always wanted to use machine learning in the browser as well. This book focuses on how Danfo.js brings data processing, analysis, and ML tools to JavaScript developers and how to make the most of this library to build data-driven applications. Starting with an overview of modern JavaScript, you’ll cover data analysis and transformation with Danfo.js and Dnotebook. The book then shows you how to load different datasets, combine and analyze them by performing operations such as handling missing values and string manipulations. You’ll also get to grips with data plotting, visualization, aggregation, and group operations by combining Danfo.js with Plotly. As you advance, you’ll create a no-code data analysis and handling system and create-react-app, react-table, react-chart, Draggable.js, and tailwindcss, and understand how to use TensorFlow.js and Danfo.js to build a recommendation system. Finally, you’ll build a Twitter analytics dashboard powered by Danfo.js, Next.js, node-nlp, and Twit.js. By the end of this app development book, you’ll be able to build and embed data analytics, visualization, and ML capabilities into any JavaScript app in server-side Node.js or the browser.

What you will learn

Perform data experimentation and analysis with Danfo.js and Dnotebook

Build machine learning applications using Danfo.js integrated with TensorFlow.js

Connect Danfo.js with popular database applications to aid data analysis

Create a no-code data analysis and handling system using internal libraries

Develop a recommendation system with Danfo.js and TensorFlow.js

Build a Twitter analytics dashboard for sentiment analysis and other types of data insights

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Building Data-Driven Applications with Danfo.js

Can$61.99

Can$61.99

Machine Learning Engineering with Python

Can$69.99

Total Can$ 193.97

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Nithin Dec 13, 2021

I have always thought data analysis can only be performed using Python's libraries but then I came across this book which explains how to use Javascript for data analysis. It begins with a comprehensive introduction of JavaScript and focuses on Danfo.js for data processing and visualization. I love that this book tries to cover most of the aspects of data analysis making it very simple for beginners to follow and also build projects to cement the lessons learned. I would suggest this book if you are interested in data analysis and wish to build applications in JavaScript as you progress through the book.

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Anonymous Nov 12, 2021

I have got the chance to read this book and the book is very nicely explained along with a rich set of examples throughout the book. The examples are written following the best JavaScript practice.I like the organization, presentation, and contents of the book a lot.

E. Leonard Nov 16, 2021

I approached this book half sceptic and half wide-eyed enthusiast. It’s fair to say python has pretty much cornered the data wrangling and ML market. It’s equally fair to say that if you intend to move out of notebooks and into deployable software on the web you’ll inevitably encounter javascript.The early segments of the book will sharpen up your javascript, allowing you to create a dev environment and introduce you to notebooks and basic conceptsFollowing the common approaches and use cases for notebooks, or Dnotebooks as they’re called here, we move into series and dataframes (pandas like capability). They cover ground familiar to Jupyter users and can be used for wrangling, analysis, ML or simply learning javascript interactively with some small, digestible changes for how consoles work with the library/browser. People who know JavaScript and python will have a fairly easy transition, if you don’t know JS you’ll learn enough here to be effective within the scope of the book. If you know JS but are new to data analysis and ML you're in for a nice ride.As the book progresses we move on to using Plotly and Danfo for a walkthrough of the DataViz side of things, again this will be familiar ground for people coming from a python ecosystem, the general usage of the APIs is the same and all the better for it. This was an enjoyable section. It was satisfying to create useful visualisations without the pain of working with raw d3.The projects presented in the book are varied for the purpose of showing off the tools, interesting enough to follow along, the explanations are clear without being dragged out, weighty enough that you won’t feel they’ve been stripped down to the point of missing the richness of the functionality being explained. Overall I enjoyed the pacing and depth.The last third of the book dives deeper into ML with tensorfow.js and builds projects around Twitter and recommender systems. Neither of these projects are truly original but again the benefit here is you can see the differences, the comfortable similarities between working in JavaScript and python ecosystems respectively. One bonus is the usage of next.JS by vercel which is a React framework growing in reach and popularity at the moment.No hesitation recommending the book. I enjoyed the read and I would refer to it in a real-world setting. This book really helps the JS developer get going with ML without having to adopt a different language. As the adage goes "What can be written in JS will eventually be written in JS".An easy 4 or 4.5 stars.

BitterPill Oct 06, 2023

Don't expect the code to actually work. It is way out of date. Somewhere along the line, the developers changed many things - read_csv is now readCSV, for example. I'm finding this throughout the book.

Building Data-Driven Applications with Danfo.js: A practical guide to data analysis and machine learning using JavaScript

What do you get with eBook?