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
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The Real Work of Data Science: 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, logistic…
Causality is a topic that has been of interest for centuries. Statisticians have been careful to emphasize that correlation is not causation as lurking variables can show apparent relationships which are misleading. The first author is a main force behind modern efforts to address causality models. He developed graphical representations linking variables and a method of intervention, called the “do” calculus, which enables the study of causal effects in observed data. The historical perspective provided in the book makes it an excellent reference to anyone interested in the topic.
'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 smoking…
In addressing decision makers, an understanding of forces affecting human behavior is essential. The author provides a comprehensive view on the work of Kahneman and Tversky mapping their journey which lead to their highly impactful behavioral economic body of knowledge. The bias they address provides
data scientists a widened perspective on their role and how to address
organizational challenges. This provides a language to recognise and address
bias inherent in human thinking. The book provides a very powerful set of tools
for data scientists who want to see behind the numbers and understand forces
affecting human behavior.
'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 young…
The book refers to the crisis in western industrialized countries in the 1980s. It emphasizes the role of management and the focus on processes for inducing changes and improvements. Deming, a statistician and physicist by training, moved his attention to management consulting, as a necessary step to ensure results and enhanced impact. The Deming message needs to be updated to the big data analytic era. Modern data scientists have powerful analytics and immense data sets to work with. They can significantly contribute to their organization’s bottom line by improving quality, productivity, and competitive performance. Deming’s book provides a context for making this happen.
Essential reading for managers and leaders, this is the classic work on management, problem solving, quality control, and more—based 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 Deming’s approach has high levels of success and sustainability. Readers today will find Deming’s insights relevant, significant, and effective in business thinking and practice. This…
A lightly technical introduction to a comprehensive framework defining and evaluating the quality of information generated by statistical analysis. It expands the role of analytics by including dimensions that affect information quality such as data resolution, data integration, operationalization, and generalizability of findings. This wide-angle perspective provides a practical checklist that has been found useful in applications. Multiple case studies enable the reader to connect to his favorite topic, but also learn from other areas.
Provides an important framework for data analysts in assessing the quality of data and its potential to provide meaningful insights through analysis Analytics and statistical analysis have become pervasive topics, mainly due to the growing availability of data and analytic tools. Technology, however, fails to deliver insights with added value if the quality of the information it generates is not assured. Information Quality (InfoQ) is a tool developed by the authors to assess the potential of a dataset to achieve a goal of interest, using data analysis. Whether the information quality of a dataset is sufficient is of practical importance…
Data and data science can be truly transformative, improving customer satisfaction, increasing profits, and empowering people. The book is about turning numbers into information and insights. To be useful, the data analysis needs to guide decisions that carry a positive impact in the workplace. The chapters cover the different steps data scientists take in organizations. Eighteen short chapters provide, with case studies, essential elements that characterize the data transformation leveraging numbers into information. It is a blueprint for the role of data scientists in organizations. Both data scientists and their managers can benefit from it.
