Building data driven applications with Danfo.js : a practical guide to data science and machine learning in JavaScript / Rising Odegua, Stephen Ayodele Oni.

Format:

Book

Author/Creator:

Odegua, Rising, author.

Oni, Stephen, author.

Language:

English

Subjects (All):

Application software--Development.

Application software.

Physical Description:

1 online resource (477 pages)

Place of Publication:

Birmingham, England : Danfopress, [2021]

Biography/History:

Odegua Rising: Rising Odegua is the co-creator of Danfo. js and Dnotebook. He is a software and machine learning engineer with experience building data-driven applications in languages such as Python and JavaScript. Rising is actively engaged in building the developer ecosystem by giving talks, providing direct/indirect mentorship, working on open source projects, hosting meetups, and writing tutorials. Oni Stephen: Stephen Oni is the co-creator of Danfo. js and Dnotebook. He is a software developer at Datopian, building data-driven platforms and tools. He is also an open source developer building tools that intersect the web and machine learning.

Summary:

Get hands-on with building data-driven applications using Danfo.js in combination with other data analysis tools and techniquesKey FeaturesBuild microservices to perform data transformation and ML model serving in JavaScriptExplore what Danfo.js is and how it helps with data analysis and data visualizationCombine Danfo.js and TensorFlow.js for machine learningBook DescriptionMost 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 learnPerform data experimentation and analysis with Danfo.js and DnotebookBuild machine learning applications using Danfo.js integrated with TensorFlow.jsConnect Danfo.js with popular database applications to aid data analysisCreate a no-code data analysis and handling system using internal librariesDevelop a recommendation system with Danfo.js and TensorFlow.jsBuild a Twitter analytics dashboard for sentiment analysis and other types of data insightsWho this book is forThis book is for data analysts, data scientists, and JavaScript developers who want to create data-driven applications in the JavaScript/Node.js environment. Intermediate-level knowledge of JavaScript programming and data science using pandas is expected.

Contents:

Cover

Title Page

Copyright and Credits

Preface

Section 1: The Basics

Chapter 1: An Overview of Modern JavaScript

Technical requirements

Understanding the difference between let and var

var allows the redeclaration of variables

var is not a blocked scope

Destructuring

Spread syntax

Spreading or unpacking an iterable into an array

Creating new objects from existing ones

Function arguments

Overview of scopes and closures

Scope

Closure

Understanding Array and Object methods

Array methods

Objects

Understanding the this property

Arrow functions

Promises and async/await

Cleaning callbacks with promises

async/await

Object-oriented programming and JavaScript classes

Classes

Inheritance

Setting up a modern JavaScript environment with transpilers

Babel

Webpack

Unit testing with Mocha and Chai

Setting up a test environment

Summary

Section 2: Data Analysis and Manipulation with Danfo.js and Dnotebook

Chapter 2: Dnotebook - An Interactive Computing Environment for JavaScript

Introduction to Dnotebook

Setup and installation of Dnotebook

Basic concepts behind interactive computing in Dnotebook

Cells

Code cells

Markdown cells

Persistence/state

Writing interactive code

Loading external packages

Loading CSV files

Getting a div container for plots

Gotchas when using a for loop

Working with Markdown cells

Creating a Markdown cell

Adding images

Headings

Lists

Saving notebooks

Chapter 3: Getting Started with Danfo.js

Why you need Danfo.js

Installing Danfo.js

Introducing Series and DataFrames

Series

DataFrames

Essential functions and methods in Danfo.js

loc and iloc indexing

Sorting

Filtering.

Arithmetic operations

Logical operations

Data loading and working with different file formats

Transforming a DataFrame into another file format

Chapter 4: Data Analysis, Wrangling, and Transformation

Transforming data

Replacing missing values

Removing duplicates

Data transformation with the map function

Data transformation with the apply function

Filtering and querying

Random sampling

Encoding DataFrames and Series

Combining datasets

DataFrame merge

Data concatenation

Series data accessors

Calculating statistics

Calculating statistics by axis

Chapter 5: Data Visualization with Plotly.js

A brief primer on Plotly.js

Using Plotly.js via a script tag

Fundamentals of Plotly.js

Data format

Configuration options for plots

Plotly layout

Creating basic charts with Plotly.js

Creating statistical charts with Plotly.js

Creating histogram plots with Plotly.js

Creating box plots with Plotly.js

Creating violin plots with Plotly.js

Chapter 6: Data Visualization with Danfo.js

Setting up Danfo.js for plotting

Adding Danfo.js to your code

Downloading a dataset for plotting

Creating line charts with Danfo.js

Creating scatter plots with Danfo.js

Creating box and violin plots with Danfo.js

Making box and violin plots for a Series

Box and violin plots for multiple columns

Box and violin plots with specific x and y values

Creating histograms with Danfo.js

Creating a histogram from a Series

Creating a histogram from multiple columns

Creating bar charts with Danfo.js

Creating a bar chart from a Series

Creating a bar chart from multiple columns

Chapter 7: Data Aggregation and Group Operations

Technical requirements.

Grouping data

Single-column grouping

Double-column grouping

Iterating through grouped data

Iterating through single- and double-column grouped data

Using the .apply method

Data aggregation of grouped data

Data aggregation on single-column grouping

Data aggregation on double-column grouping

A simple application of groupby on real data

Section 3: Building Data-Driven Applications

Chapter 8: Creating a No-Code Data Analysis/Handling System

Setting up the project environment

Structuring and designing the app

App layout and the DataTable component

Implementing DataTable components

File upload and state management

Creating different DataFrame operation components

Implementing the Describe component

Implementing the Query component

Implementing the Df2df component

Implementing the Arithmetic component

Implementing the chart component

Implementing the ChartPlane component

Implementing the ChartViz component

Integrating ChartViz and ChartPlane into App.js

Chapter 9: Basics of Machine Learning

Introduction to machine learning

A simple analogy of a machine learning system

Why machine learning works

Objective functions

Evaluation metrics

Machine learning problems/tasks

Supervised learning

Unsupervised learning

Machine learning in JavaScript

Applications of machine learning

Resources to understand machine learning in depth

Chapter 10: Introduction to TensorFlow.js

What is TensorFlow.js?

Installing and using TensorFlow.js

Setting up TensorFlow.js in the browser

Installing TensorFlow.js in Node.js

Tensors and basic operations on tensors

Creating tensors

Operating on tensors.

Building a simple regression model with TensorFlow.js

Setting up your environment locally

Retrieving and processing the training dataset

Creating models with TensorFlow.js

Creating a simple three-layer regression model

Training the model with the processed dataset

Making predictions with the trained model

Chapter 11: Building a Recommendation System with Danfo.js and TensorFlow.js

What is a recommendation system?

Collaborative filtering approach

Hybrid filtering approach

The neural network approach to creating a recommendation system

Building a movie recommendation system

Setting up your project directory

Building the recommendation model

Training and saving the recommendation model

Making movie recommendations with the saved model

Chapter 12: Building a Twitter Analysis Dashboard

Building the backend

Building the Twitter API

Building the text sentiment API

Building the frontend

Creating the Search component

Creating the ValueCounts component

Creating a plot component for sentiment analysis

Creating a Table component

Chapter 13: Appendix: Essential JavaScript Concepts

Quick overview of JavaScript

Understanding the fundamentals of JavaScript

Declaring variables

Data types

Conditional branching and loops

JavaScript functions

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Index.

Notes:

Includes index.

Description based on print version record.

ISBN:

9781801078412

1801078416

OCLC:

1276856226