Here are 23 books that Information Quality fans have personally recommended if you like
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Leadership is the key ingredient that moves the needle. Each of us has the rightâand dutyâto be a leader of our life and family, organization and society, and to inspire others for something bigger than ourselves, something that has not been done before. But why am I so passionate about leadership? Why is it the focus of my books, my teaching, my company? It all started in my youth: The defining moment came after my sisterâs death to a heroin overdose. I stood at my sisterâs grave and decided I would never be a victim of circumstancesâI would pursue self-determination. Leadership is the exact opposite of victimhood.Â
Any book by Michael Lewis is fun and educational, but this one I couldnât put down. In 2002, for the first time, the Nobel prize for economics did not go to an economist but to a psychologistâDaniel Kahnemanâwho had single-handedly (with his genius collaborator Amos Tversky) disrupted the economics profession and its core theoriesâmuch like Einstein had transformed our understanding of reality and Freud of ourselvesâand created an entirely new field called behavioral economics.
This is the story of a remarkable partnership of two eminent scientists who brought about this revolution. Tversky and Kahneman had such a close relationship that even their wives became jealous. This book might make you laugh and cry. And you might learn much about cutting-edge economics and our chronic biases.
'Michael Lewis could spin gold out of any topic he chose ... his best work ... vivid, original and hard to forget' Tim Harford, Financial Times
'Gripping ... There is war, heroism, genius, love, loss, discovery, enduring loyalty and friendship. It is epic stuff ... Michael Lewis is one of the best non-fiction writers of our time' Irish Times
From Michael Lewis, No.1 bestselling author of The Big Short and Flash Boys, this is the extraordinary story of the two men whose ideas changed the world.
Daniel Kahneman and Amos Tversky met in war-torn 1960s Israel. Both were gifted youngâŚ
It is April 1st, 2038. Day 60 of China's blockade of the rebel island of Taiwan.
The US government has agreed to provide Taiwan with a weapons system so advanced that it can disrupt the balance of power in the region. But what pilot would be crazy enough to runâŚ
I was trained as a mathematician but have always been motivated by problem-solving challenges. Statistics and analytics combine mathematical models with statistical thinking. My career has always focused on this combination and, as a statistician, you can apply it in a wide range of domains. The advent of big data and machine learning algorithms has opened up new opportunities for applied statisticians. This perspective complements computer science views on how to address data science. The Real Work of Data Science, covers 18 areas (18 chapters) that need to be pushed forward in order to turning data into information, better decisions, and stronger organizations
The text covers classic statistical inference, early computer-age methods, and twenty-century topics. This puts a unique perspective on current analytic technologies labeled machine learning, artificial intelligence, and statical learning. The examples used provide a powerful description of the methods covered and the compare and contrast sections highlight the evolution of analytics. This book by Efron and Hastie is a natural follow-up source for readers interested in more details.
The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logisticâŚ
I am an academic researcher and an avid non-fiction reader. There are many popular books on science or music, but itâs much harder to find texts that manage to occupy the space between popular and professional writing. Iâve always been looking for this kind of book, whether on physics, music, AI, or math â even when I knew that as a non-pro, I wouldnât be able to understand everything. In my new book Iâve been trying to accomplish something similar: A book that can intrigue readers who are not professional economic theorists, that they will find interesting even if they canât follow everything.
In the ongoing debates over artificial general intelligence (AGI), Judea Pearl is taking a firm stand: He argues that an intelligent robot should be able to reason about causality and that the currently fashionable approaches to AI miss this aspect.
A celebrated AI researcher and a Turing Prize laureate, Pearl has developed an amazingly original approach to this problem. This book is a high-end popular exposition of his approach.
But itâs so much more than that. Itâs a history of statistics and its conflicted attitude to causality. Itâs a story of heroes (or villains?) in this history. And itâs a scientific autobiography that describes Pearlâs journey. Pearl likes picking fights with the AI community, statisticians, or economists. Heâs boastful, provocative, extremely intelligent, and knows how to tell a story.
'Wonderful ... illuminating and fun to read' - Daniel Kahneman, winner of the Nobel Prize and author of Thinking, Fast and Slow
'"Pearl's accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence and have redefined the term "thinking machine"' - Vint Cerf, Chief Internet Evangelist, Google, Inc.
The influential book in how causality revolutionized science and the world, by the pioneer of artificial intelligence
'Correlation does not imply causation.' This mantra was invoked by scientists for decades in order to avoid taking positions as to whether one thing caused another, such as smokingâŚ
A Duke with rigid opinions, a Lady whose beliefs conflict with his, a long disputed parcel of land, a conniving neighbour, a desperate collaboration, a failure of trust, a love found despite it all.
Alexander Cavendish, Duke of Ravensworth, returned from war to find that his father and brother hadâŚ
I am an organizational psychologist interested in how leadership decision-making influences organizational culture. Iâve studied this for the last 5 years and developed models that pinpoint specific decisions that led to specific cultural attributes and related performance outcomes. I led a team that worked with the top 100 leaders at NASA after the Columbia Space Shuttle disaster.Â
Deming showed me how to think about organizational performance improvement. I was moving from a clinical psychologist in private practice to an organizational psychologist helping companies develop change strategies. I had studied and loved statistical variation in the context of scientific research, but not in the context of addressing real-world challenges.Â
But Deming does something very surprising. He starts by understanding variation and then moves on to understanding organizational culture. Not the theoretical frameworks we all know, but the work world from the view of the front-line employee. Demingâs insight is that the central challenge of culture change is understanding the view of people closest to the work, the ones who perform operations.
They are not motivated by slogans and lofty ideas but by producing great products and services. Taking pride in the work they are doing is central to performance; lost by management fads and enhanced by doing itâŚ
Essential reading for managers and leaders, this is the classic work on management, problem solving, quality control, and moreâbased on the famous theory, 14 Points for Management
In his classic Out of the Crisis, W. Edwards Deming describes the foundations for a completely new and transformational way to lead and manage people, processes, and resources. Translated into twelve languages and continuously in print since its original publication, it has proved highly influential. Research shows that Demingâs approach has high levels of success and sustainability. Readers today will find Demingâs insights relevant, significant, and effective in business thinking and practice. ThisâŚ
I am Wes McKinney, creator of the Python pandas project and author of Python for Data Analysis. I have been using Python for data work since 2007 and have worked extensively in the open source community to build accessible and fast data processing tools for Python programmers.
This is a great follow-up book to Python Data Science Handbook.
Co-authored by one of the core developers of scikit-learn, this provides a deeper introduction to doing machine learning work in Python. This will give you a solid foundation to be able to move on later to deeper topics including deep learning or other AI topics.
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than theâŚ
I am Wes McKinney, creator of the Python pandas project and author of Python for Data Analysis. I have been using Python for data work since 2007 and have worked extensively in the open source community to build accessible and fast data processing tools for Python programmers.
While this book has a good amount of overlap with my book, it provides a valuable introduction to scikit-learn, one of the most popular libraries for machine learning in Python. There is also excellent content to improve your data visualization skills with matplotlib.
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all-IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this isâŚ
The Duke's Christmas Redemption
by
Arietta Richmond,
A Duke who has rejected love, a Lady who dreams of a love match, an arranged marriage, a house full of secrets, a most unneighborly neighbor, a plot to destroy reputations, an unexpected love that redeems it all.
Lady Charlotte Wyndham, given in an arranged marriage to a man sheâŚ
I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada. Â
Another practical book that I highly recommend. Its intuitive structure is the first thing I like about it. It gives you a comprehensive walkthrough of the ML workflow, from data exploration to learning. It covers abundant practical guides that get you prepared for real world challenges, such as how to handle outliers and to impute missing data. As a ML practitioner, I appreciate the dedicated case studies throughout the entire book. They really excite learners for future real world applications.
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the applicationâŚ
I am a leader in analytics and AI strategy, and have a broad range of experience in aviation, energy, financial services, and the public sector. I have worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics.
This is a foundational book on analytics and data science as a business function and helped to shape the development of the practice. It provides a view of the discipline through a business lens and avoids deep technical examinations. Though much has changed in the 15 years since it was originally published, it is still essential reading for a leader in the field. No book since has captured as well the competitive differentiation that analytics provides.
You have more information at hand about your business environment than ever before. But are you using it to "out-think" your rivals? If not, you may be missing out on a potent competitive tool. In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling. Exemplars of analytics are using newâŚ
I am a leader in analytics and AI strategy, and have a broad range of experience in aviation, energy, financial services, and the public sector. I have worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics.
Not everybody needs to be a data scientist, but everybody does need to be data literate. Without an intentional focus on evangelism and building a strong data culture in your organization it will be an uphill battle to make meaningful change. This book helps individuals and leaders to understand what data literacy is, and how we can build it like any other skill.
In the fast moving world of the fourth industrial revolution not everyone needs to be a data scientist but everyone should be data literate, with the ability to read, analyze and communicate with data.
It is not enough for a business to have the best data if those using it don't understand the right questions to ask or how to use the information generated to make decisions. Be Data Literate is the essential guide to developing the curiosity, creativity and critical thinking necessary to make anyone data literate, without retraining as a data scientist or statistician.
With learnings to showâŚ
This book follows the journey of a writer in search of wisdom as he narrates encounters with 12 distinguished American men over 80, including Paul Volcker, the former head of the Federal Reserve, and Denton Cooley, the worldâs most famous heart surgeon.
In these and other intimate conversations, the bookâŚ
I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada. Â
This could be the first stop of your brand new machine learning journey. I personally like how the technical concept is translated into plain English â each chapter starts with a high-level overview of a ML algorithm or methodology, concise and clear, followed by lots of visual examples and real world scenarios. I can guarantee you wonât get lost halfway. The book focuses on getting you introduced to ML with minimal math. But if you want to grasp some more of math, the next book I recommend is waiting for you.Â
NOTICE: To buy the newest edition of this book (2021), please search "Machine Learning Absolute Beginners Third Edition" on Amazon. The product page you are currently viewing is for the 2nd Edition (2017) of this book.
Featured by Tableau as the first of "7 Books About Machine Learning for Beginners."
Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?
Well, hold on there...
Before you embark on your epic journey, there are some high-level theory and statistical principles to weave through first. But rather than spendâŚ
