JavaScript (Node.js) for Data Science: Data Analysis and Machine Learning on the Server

The world of Data Science is traditionally associated with Python and R, but modern JavaScript (especially the Node.js environment) offers powerful tools for data analysis, machine learning, and visualization. This book is the first comprehensive guide that proves: you can build data pipelines, train models, and create interactive reports without leaving the JavaScript ecosystem.

What is this book about?

The book covers the full Data Science cycle on Node.js: from data collection and cleaning to deploying ready-made ML models. You will learn how to use specialized libraries (Danfo.js, TensorFlow.js, Plotly.js), work with large volumes of data, build charts, and run neural networks directly on the server or in the browser.

• Chapters 1–2: Introduction to Data Science on Node.js. Installing the stack (Danfo.js, TensorFlow.js, numjs). Working with CSV, JSON, databases.
• Chapters 3–4: Data cleaning and transformation. Statistical analysis, handling missing values, filtering, aggregation.
• Chapters 5–6: Data visualization: histograms, scatter plots, time series using Plotly.js and Chart.js.
• Chapters 7–8: Machine learning: linear regression, classification (k-NN, logistic regression), clustering (K-means) with TensorFlow.js.
• Chapters 9–10: Neural networks and deep learning. Creating and training models on Node.js. Exporting/importing models.
• Chapters 11–12: Deployment: REST API for ML models (Express.js), integration with databases, creating dashboards.

Who is this book for?

• JavaScript developers (Node.js) who want to master Data Science without learning a new language.
• Analysts familiar with SQL and basic statistics who wish to switch to the JavaScript stack.
• Students and professionals looking for an alternative to Python for data analysis.
• Anyone who wants to create ML products in a single language (full-stack Data Science).

What will you learn?

• How to process and analyze data volumes of up to 1 million rows using Danfo.js.
• How to build interactive charts and dashboards without using Jupyter Notebook.
• How to train machine learning models (linear regression, decision trees, neural networks) on Node.js.
• How to deploy a ready-made model via a REST API and integrate it into a web application.
• How to use TensorFlow.js for browser-based ML — without a server-side component.

Why should you read it?

JavaScript is the most common programming language. This book breaks the stereotype that Data Science is only accessible to Pythonists. You will gain practical skills, ready-made cases, and code templates that can be immediately applied in your work. The examples are based on real-world tasks: log processing, sales forecasting, text and image classification.

Regardless of your current level, you will learn to think like a data scientist while staying in the familiar Node.js environment. The book contains exercises, self-assessment questions, and links to the code repository.