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

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
	Technical requirements
	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
	Summary
Chapter 3: Getting Started with Danfo.js
	Technical requirements
	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
	Summary
Chapter 4: Data Analysis, Wrangling, and Transformation
	Technical requirements
	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
	Summary
Chapter 5: Data Visualization with Plotly.js
	Technical requirements
	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
	Summary
Chapter 6: Data Visualization with Danfo.js
	Technical requirements
	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
	Summary
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
	Summary
Section 3: Building Data-Driven Applications
Chapter 8: Creating a No-Code Data Analysis/Handling System
	Technical requirements
	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
	Summary
Chapter 9: Basics of Machine Learning
	Technical requirements
	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
	Summary
Chapter 10: Introduction to TensorFlow.js
	Technical requirements
	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
	Summary
Chapter 11: Building a Recommendation System with Danfo.js and TensorFlow.js
	Technical requirements
	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
		Retrieving and processing the training dataset
		Building the recommendation model
		Training and saving the recommendation model
		Making movie recommendations with the saved model
	Summary
Chapter 12: Building a Twitter Analysis Dashboard
	Technical requirements
	Setting up the project environment
	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
	Summary
Chapter 13: Appendix: Essential JavaScript Concepts
	Technical requirements
	Quick overview of JavaScript
	Understanding the fundamentals of JavaScript
		Declaring variables
		Data types
		Conditional branching and loops
		JavaScript functions
	Summary
Other Books You May Enjoy
Index