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Building Data-Driven Applications with Danfo.js

Building Data-Driven Applications with Danfo.js

By : Odegua, Oni
3.8 (4)
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Building Data-Driven Applications with Danfo.js

Building Data-Driven Applications with Danfo.js

3.8 (4)
By: Odegua, Oni

Overview of this book

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.
Table of Contents (18 chapters)
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1
Section 1: The Basics
3
Section 2: Data Analysis and Manipulation with Danfo.js and Dnotebook
10
Section 3: Building Data-Driven Applications

Why you need Danfo.js

To successfully bring a machine learning project written in Python to the web, there are a lot of processes and tasks to be carried out, things such as model deployment, creating API routes with frameworks such as Flask, FastAPI, or Django, and then using JavaScript to send HTTP requests to the model. You can clearly observe that the process involves a lot of JavaScript. It would be super awesome if we could perform all these processes in just JavaScript, wouldn't it? Well, the good news is that we can.

Over the past years, browsers have steadily increased in computational power and can support highly intensive computation, hence giving JavaScript the edge to challenge Python when it comes to data-intensive tasks.

With the help of Node.js, JavaScript has access to the GPU available on local computers, giving us the ability to undergo a full-stack machine learning project using Node.js for the backend and pure JavaScript for the frontend.

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