---
product_id: 456777455
title: "Optimization for Data Analysis"
price: "€ 106.37"
currency: EUR
in_stock: true
reviews_count: 2
url: https://martinique.desertcart.com/products/456777455-optimization-for-data-analysis
store_origin: MQ
region: Martinique
---

# Focus on data science & machine learning algorithms 300 pages of core optimization knowledge Covers gradient & stochastic gradient methods Optimization for Data Analysis

**Price:** € 106.37
**Availability:** ✅ In Stock

## Summary

> 📈 Unlock the math behind data mastery — don’t get left behind!

## Quick Answers

- **What is this?** Optimization for Data Analysis
- **How much does it cost?** € 106.37 with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [martinique.desertcart.com](https://martinique.desertcart.com/products/456777455-optimization-for-data-analysis)

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## Why This Product

- Free international shipping included
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## Key Features

- • **Compact Yet Dense:** 300 pages of tightly packed, essential optimization theory
- • **Data Science Focused:** Tailored content for practical optimization in analytics
- • **Hardcover Durability:** A professional, long-lasting reference for your bookshelf
- • **Comprehensive Algorithm Coverage:** From gradient descent to back-propagation, all in one place
- • **Core Optimization Techniques Unpacked:** Master the algorithms powering data science and ML

## Overview

Optimization for Data Analysis by Stephen J. Wright is a 300-page hardcover book that delves into fundamental optimization algorithms crucial for data science and machine learning. It covers key methods such as gradient descent, stochastic gradient, coordinate descent, and back-propagation, providing a compact, theory-focused resource aimed at students and practitioners seeking a deep understanding of optimization techniques relevant to data analysis.

## Description

Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundations of the analysis of nonsmooth functions and optimization duality; and the back-propagation approach, relevant to neural networks.

Review: poorly written & explained - very poorly written & explained.. sections of it are completely incomprehensible ...you cannot easily self-study this book
Review: Terrible for people who are new to the field of optimization - While this book contains a collection of fundamental results in the field, it is difficult to recommend this book to learn the subject. The text lacks clarity, and seems to move from one result to the next without much explanation. The proofs are hard to follow, often omitting important steps, and, to a reader who is unfamiliar with the field, seemingly draw conclusions from thin air. In addition, authors often use unfamiliar concepts and notation without explaining their meaning, sometimes making the text impenetrable to people who are new to optimization theory.

## Features

- Author: Wright, Stephen J.
- Language: english
- Binding Type: hardcover
- Genre: Optimization
- Number of pages: 300

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #430,528 in Books ( See Top 100 in Books ) #959 in Databases & Big Data #38,624 in Reference |
| Customer Reviews | 3.8 out of 5 stars 17 Reviews |

## Images

![Optimization for Data Analysis - Image 1](https://m.media-amazon.com/images/I/61+xrLBRt1L.jpg)

## Customer Reviews

### ⭐⭐ poorly written & explained
*by S***R on 28 February 2024*

very poorly written & explained.. sections of it are completely incomprehensible ...you cannot easily self-study this book

### ⭐⭐ Terrible for people who are new to the field of optimization
*by E***S on 9 March 2025*

While this book contains a collection of fundamental results in the field, it is difficult to recommend this book to learn the subject. The text lacks clarity, and seems to move from one result to the next without much explanation. The proofs are hard to follow, often omitting important steps, and, to a reader who is unfamiliar with the field, seemingly draw conclusions from thin air. In addition, authors often use unfamiliar concepts and notation without explaining their meaning, sometimes making the text impenetrable to people who are new to optimization theory.

## Frequently Bought Together

- OPTIMIZATION FOR DATA ANALYSIS
- Numerical Optimization (Springer Series in Operations Research and Financial Engineering)
- Lectures on Convex Optimization

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*Product available on Desertcart Martinique*
*Store origin: MQ*
*Last updated: 2026-08-28*