JavaScript (Node.js) for Data Science: Data Processing and Machine Learning

The world of Data Science is traditionally associated with Python and R. But what if your primary tool is JavaScript, and the Node.js ecosystem has become second nature to you? The book "JavaScript (Node.js) for Data Science" breaks the stereotype that serious work with data is only possible with "specialized" languages.

This practical guide will take you through all stages of a Data Science project, using exclusively the capabilities of the Node.js platform. You won't just read theory — you'll build working data processing pipelines, learn to extract insights, and build predictive models without leaving your familiar JavaScript environment.

What you will learn and master:

• Data collection and cleaning: web scraping (Cheerio, Puppeteer), working with APIs, reading CSV/JSON/Excel (Papa Parse, xlsx), removing duplicates and filling in missing values.
• Data manipulation: efficient transformations using lodash and Danfo.js (a pandas equivalent for JS), grouping, filtering, aggregation.
• Visualization: creating interactive charts and dashboards with Plotly.js, Chart.js, and D3.js directly in the browser or on the server.
• Statistical analysis: calculating correlations, distributions, hypothesis testing using simple-statistics and jStat.
• Machine learning: implementing linear regression, classification (KNN, Naive Bayes), and clustering (K-Means) with ml.js and brain.js (neural networks) libraries.
• Deployment: packaging models into a REST API (Express) and creating full-fledged web applications for data analysis.

Who this book is for:

• Frontend and Fullstack developers who want to expand their skills in Data Science without learning a new language.
• Node.js programmers who need to analyze logs, metrics, or user data.
• Students and professionals who want to see an alternative perspective on data analysis through the lens of JavaScript.
• Anyone looking for an easy entry into Data Science that doesn't require setting up Anaconda and Python virtual environments.

The book is written in a lively style, contains many ready-to-run code examples, and exercises to reinforce the material. You will start with simple file reading and progress to creating a neural network model for forecasting that runs on Node.js.

Discover Data Science without switching stacks — JavaScript provides all the tools for this!