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25 Best Nonfiction Books Of All Time According To Principal Data Scientist-Kirk Borne

In the ever-evolving field of data science and technology, staying ahead of the curve is essential.

To navigate this complex landscape, it's crucial to have access to the most insightful and informative resources available.

If you're looking to expand your knowledge and enhance your skills, you're in for a treat!

Renowned Principal Data Scientist Kirk Borne has curated a list of the 25 great nonfiction books he recommends.

Affiliate Disclaimer: This post features Amazon affiliate links, which means I may earn a small commission if you make a purchase through these links.

In this unique book, Robert S. Sutor takes readers on an exciting journey into the world of Python programming and quantum computing. It offers an accessible introduction to both Python and the fascinating realm of quantum computation. Readers will discover how Python can be used to explore the intricate concepts of quantum computing, making this book an excellent starting point for those intrigued by the potential of quantum technologies.

Authored by the esteemed Kirk Borne, this book is a masterclass in research communication. It goes beyond the technical aspects of research and focuses on the art of effective communication. Kirk Borne shares invaluable insights on how to present research findings in a compelling and impactful way, ensuring that your work reaches a broader audience and drives meaningful change.

Jonathan Schwabish's book is a comprehensive guide tailored specifically for scholars, researchers, and professionals who want to enhance their presentation skills. It provides practical strategies for creating engaging and informative presentations that captivate audiences and effectively convey complex information. With a focus on data visualization and storytelling, this book equips readers with the tools they need to make their presentations stand out.

Ulrika Jagare's book delves into the crucial intersection of artificial intelligence and business operations. It offers a holistic perspective on AI, exploring not only the technical aspects but also the strategic and organizational considerations. Readers will gain insights into how to harness the power of AI to drive innovation and create tangible business value.

"Building Machine Learning and Deep Learning Models on Google Cloud Platform" by Ekaba Bisong (Sep 2019) is a beginner-friendly guide to machine learning on GCP. Bisong simplifies complex concepts, emphasizes hands-on learning with GCP tools, and covers ethical considerations, making it an ideal starting point for newcomers.

Gilbert Eijkelenboom's book addresses a critical aspect of success for analytical thinkers: effective communication and interpersonal skills. It recognizes that being technically proficient is not enough in today's data-driven world. The book offers practical advice on how analytical thinkers can improve their people skills, foster better relationships, and advance both their careers and personal lives.

Robert B. Cialdini's classic work explores the science of influence and persuasion. Drawing on extensive research, Cialdini identifies six key principles of persuasion, providing real-world examples and practical applications. Readers will learn how to ethically apply these principles to influence decision-making and achieve desired outcomes, making it an invaluable resource for data professionals seeking to persuade and inform.

Building upon his previous work, Robert Cialdini introduces the concept of "pre-suasion," which involves shaping the context and setting the stage before delivering a persuasive message. This book offers advanced strategies for influencing and persuading others by strategically framing information, making it a must-read for those looking to refine their persuasion skills.

Nobel laureate Daniel Kahneman presents a captivating exploration of human thought processes. He introduces the concept of two thinking systems: "fast" and intuitive thinking and "slow" and deliberate thinking. Through numerous examples, Kahneman exposes the cognitive biases that influence decision-making and provides valuable insights into how individuals can make more rational choices.

Jonathan Schwabish's expertise in data visualization shines through in this book, which is tailored for scholars, researchers, and professionals who want to master the art of data visualization. It offers comprehensive guidance on creating clear and compelling data visualizations, allowing data professionals to effectively communicate complex information to diverse audiences.

Jesse Anderson's book is a vital resource for organizations striving to harness the power of data. It provides a unified management model for data-focused teams, emphasizing the importance of collaboration, alignment, and effective leadership. Readers will gain valuable insights into building and managing successful data teams that drive innovation and business growth.

Stefan Jansen's book is an essential read for those interested in the convergence of machine learning and finance. It offers practical insights into building predictive models to extract signals from financial markets and alternative data sources. With a focus on Python and systematic trading strategies, it equips readers with the tools to navigate the dynamic world of algorithmic trading.

Michael Lewrick's book explores the transformative power of design thinking in the digital age. It provides a playbook for fostering innovation across teams, products, services, businesses, and ecosystems. Readers will discover how to apply design thinking principles to solve complex problems and drive meaningful digital transformations.

Yuxi Liu's book is a practical guide for aspiring data scientists and machine learning enthusiasts. It offers hands-on examples and real-world applications, demonstrating how to build intelligent systems using Python and popular machine learning libraries such as TensorFlow 2, PyTorch, and scikit-learn. Readers will gain a deep understanding of machine learning techniques and their practical implementation.

Mark Treveil's book addresses the critical challenges of scaling machine learning in large enterprises. It introduces the concept of MLOps (Machine Learning Operations), offering insights into best practices for managing, deploying, and monitoring machine learning models in complex organizational environments. It equips data professionals with the knowledge to navigate the complexities of bringing machine learning to the enterprise level.

On Amazon

Alberto Artasanchez's comprehensive guide explores the world of artificial intelligence and its practical applications using Python. This book covers a wide range of AI techniques, from natural language processing to computer vision, and provides hands-on examples for building intelligent applications. It's an indispensable resource for those looking to leverage AI in their projects.

Hadelin de Ponteves' book offers an engaging and hands-on introduction to artificial intelligence, machine learning, reinforcement learning, and deep learning using Python. It's designed for beginners and provides a fun and interactive learning experience, making complex AI concepts accessible and enjoyable.

Andriy Burkov's concise guide condenses the essentials of machine learning into a hundred pages. It provides a streamlined yet comprehensive overview of machine learning concepts, algorithms, and techniques. It's an efficient resource for gaining a deep understanding of machine learning without getting lost in lengthy academic texts.

Tilman M. Davies' book is an ideal starting point for those interested in learning the R programming language and its applications in statistics. It offers a beginner-friendly introduction to R, equipping readers with the skills to perform data analysis and statistical modeling.

Valliappa Lakshmanan's book focuses on practical solutions to common challenges encountered in the machine learning lifecycle. It covers data preparation, model building, and MLOps, providing valuable insights and best practices for data professionals working on real-world projects.

On Amazon

Charu C. Aggarwal's book delves into the fascinating field of outlier analysis. It explores techniques and methodologies for identifying and handling outliers in data, which is essential for data quality and anomaly detection in various domains. The book equips readers with the knowledge to detect and interpret unusual data points effectively.

Abhishek Thakur's book is a practical guide for tackling a wide range of machine learning problems. It provides a structured approach to problem-solving, offering insights into data preprocessing, model selection, and evaluation techniques. Readers will learn valuable strategies for addressing diverse machine learning challenges effectively.

Jon Krohn's book takes a visually engaging and interactive approach to deep learning. It offers readers the opportunity to explore and experiment with deep learning concepts using visual explanations and interactive coding examples. This book is an excellent resource for those looking to demystify the complex world of deep learning.

Jeff Patton's book introduces the concept of user story mapping, a valuable technique in agile development for understanding and prioritizing user needs. It guides readers through the process of discovering the complete user story and building products that align with user expectations. It's an essential resource for data professionals involved in product development and design.

Closing our list is Richard H. Thaler's exploration of behavioral economics and the concept of "nudging." Thaler and his co-author Cass Sunstein delve into how subtle interventions, or "nudges," can influence decision-making in domains such as health, wealth, and happiness. The book provides fascinating insights into human behavior and offers practical implications for designing policies and systems that promote better decision-making.


If you enjoyed these book recommendations, check out more similar list on my on my blog —

These six nonfiction books, recommended by American philosopher Daniel Dennett, offer a diverse range of insights into some of the most pressing issues of our time.

These 25 books offer a diverse range of insights and expertise, covering technical skills, communication, psychology, business strategy, and more.

They are essential resources for data professionals seeking to excel in their careers and remain at the forefront of the rapidly evolving field of data science.

Whether you're a beginner or an experienced practitioner, these books provide valuable knowledge and guidance for your journey in the world of data and technology.


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