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Key benefits

Learn practical data science combined with data theory to gain maximum insights from data

Discover methods for deploying actionable machine learning pipelines while mitigating biases in data and models

Explore actionable case studies to put your new skills to use immediately

Purchase of the print or Kindle book includes a free PDF eBook

Description

Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights. Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you’ll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data. With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You’ll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you’ll explore medium-level data governance, including data provenance, privacy, and deletion request handling. By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.

Who is this book for?

If you are an aspiring novice data scientist eager to expand your knowledge, this book is for you. Whether you have basic math skills and want to apply them in the field of data science, or you excel in programming but lack the necessary mathematical foundations, you’ll find this book useful. Familiarity with Python programming will further enhance your learning experience.

What you will learn

Master the fundamentals steps of data science through practical examples

Bridge the gap between math and programming using advanced statistics and ML

Harness probability, calculus, and models for effective data control

Explore transformative modern ML with large language models

Evaluate ML success with impactful metrics and MLOps

Create compelling visuals that convey actionable insights

Quantify and mitigate biases in data and ML models

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

$52.99

Exploratory Data Analysis with Python Cookbook

$49.99

$39.99

Total $ 142.97

FAQs

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What is the delivery time and cost of print books?

Shipping Details

USA:

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

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Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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O Feb 24, 2024

Appreciate all the code examples and the up to date GitHub

Amazon Verified review

Steven Fernandes May 07, 2024

This book is a fantastic resource for anyone diving into data science. It effectively bridges the gap between theoretical math and practical programming through engaging examples and clear explanations. The sections on probability, calculus, and machine learning models are particularly enlightening. I especially appreciated the deep dive into modern machine learning techniques, including large language models, and the practical advice on using metrics and MLOps to evaluate ML projects. The guidance on creating visuals and addressing data biases is also invaluable. Highly recommend for those looking to enhance their data science skills!

Om S May 09, 2024

Principles of Data Science is a book that helps you understand data and use it to make decisions. It teaches you how to clean data, find important patterns, and make predictions using machine learning. The book covers basic to advanced topics, including how to handle biases and use big models like GPT and BERT. It's perfect for beginners who know some Python and want to get better at using data in real projects.

H2N Mar 11, 2024

This book is for new data scientists. Each chapter is well-structured, offering clear explanations and practical examples. The inclusion of Python code snippets helps the learning experience, making it easier to apply the concepts in real-world scenarios. However, the chapters on advanced statistics and probability may disappoint those expecting more depth, as they assume a level of knowledge common in the data science field. Overall, this book is a valuable resource for aspiring data scientists looking to build a solid foundation in the field.

Principles of Data Science: A beginner's guide to essential math and coding skills for data fluency and machine learning , Third Edition

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