I’m an applied statistician and academic researcher/lecturer at New Zealand’s oldest university – the University of Otago. R facilitates everything I do – research, academic publication, and teaching. It’s the latter part of my job that motivated my own book on R. From first-year statistics students who have never seen R to my own Ph.D. students using R to implement novel and highly complex statistical methods and models, my experience is that all ultimately love the ease with which the R language permits exploration, visualisation, analysis, and inference of one’s data. The ever-growing need in today’s society for skilled statisticians and data scientists means there's never been a better time to learn this essential language.
From well-known authorities in the R-sphere (including a former R Core Team member), this is a long-standing text whose first edition was one of the early books intended to teach R to beginners. It provides concise instructions and examples on how R is used as a programming language before focusing on 'number-crunching' statistical methods that are typically seen as computationally intensive. One of the notable features of this book is the statistical methods at hand are not just illustrated using 'black-box' code--the reader is provided with the necessary mathematical detail to understand what's going on behind the scenes for those that are so inclined.
This third edition of Braun and Murdoch's bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the Box-Muller and Metropolis-Hastings algorithms. New examples and exercises have been added throughout. This is the only introduction you'll need to start programming in R, the computing standard for analyzing data. This book comes with real R code that teaches the standards of the language. Unlike other introductory books on the R system, this book emphasizes portable programming skills that apply to most…
A great way to see how AI is being used by companies and not just the future predictions of how AI could be used. Made me more open to how AI will change my industry (retail) and how people can use it to make better decisions. It kicked off my current journy to become more AI aware. As always, I appreciate anything that Thomas Davenport writes.
A fascinating look at the trailblazing companies using artificial intelligence to create new competitive advantage, from the author of the business classic, Competing on Analytics, and the head of Deloitte's US AI practice.
Though most organizations are placing modest bets on artificial intelligence, there is a world-class group of companies that are going all-in on the technology and radically transforming their products, processes, strategies, customer relationships, and cultures.
Though these organizations represent less than 1 percent of large companies, they are all high performers in their industries. They have better business…
I’m an applied statistician and academic researcher/lecturer at New Zealand’s oldest university – the University of Otago. R facilitates everything I do – research, academic publication, and teaching. It’s the latter part of my job that motivated my own book on R. From first-year statistics students who have never seen R to my own Ph.D. students using R to implement novel and highly complex statistical methods and models, my experience is that all ultimately love the ease with which the R language permits exploration, visualisation, analysis, and inference of one’s data. The ever-growing need in today’s society for skilled statisticians and data scientists means there's never been a better time to learn this essential language.
An authoritative tome on R. This book is the ultimate reference guide, heavy on statistical methods from the simple to the advanced. Of the 29 chapters, only the first five chapters or so have R syntactical and programming skills as their main focus; the remaining content highlights the many and varied statistical techniques R is capable of. I think this is a fantastic book to have on the shelf for people who are likely to need R and its contributed packages for a variety of different statistical analyses, but might not know where to initially start for any given statistical method.
Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: * Features full colour text and extensive graphics throughout. * Introduces a clear structure with numbered section headings to help readers locate information more efficiently. * Looks at the evolution of R over the…
I have worked in the field of machine learning and predictive analytics for many years. Having started out as a technical specialist, I have become increasingly interested in the legal, ethical, and social aspects of these subjects. This is because it is these “soft issues” that often determine how successful these technologies are in practice and if they are viewed as a force for good or evil in wider society. This has led me to write several books focusing on the practical and cultural aspects of these subjects and how best to apply them for the benefit of business, individuals, and wider society.
Many writers have discussed the dangers that artificial intelligence and machine learning represent to our livelihoods, and how clever computers and autonomous robots will supplant us all in the workplace. What I like about this book is that it provides an alternative, and very optimistic, view of how these new technologies are being deployed. The authors present a future based on a partnership, in which artificial intelligence-based tools work in tandem with human workers, enhancing what individuals can do in the workplace rather than replacing them.
AI is radically transforming business. Are you ready?
Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now--in software that senses what we need, supply chains that "think" in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on?
In Human + Machine, Accenture leaders Paul R. Daugherty and H. James (Jim) Wilson show…
I’ve been making web pages since the World Wide Web began in the mid-1990s. Back then, the web was visually quite sparse. It wasn’t until the late 2000s that new browser capabilities let the web getvisually interestingand an exciting place for interactive graphics. Graphics are great: they can be informational (like charts and maps) or purely aesthetic. My personal journey of learning to code interactive graphics has been so rewarding that I’ve shared the love with others through teaching creative coding workshops and undergraduate courses. If you’re new to coding or computer graphics, I hope you’ll give one of these books a try!
If I were getting started with coding graphics today, I would start with this book, hands down. Learning p5 is the easiest way to create interactive graphics that run in a web browser, and this book is a very friendly, accessible, and beautifully illustrated introduction to coding graphics with p5.js—no prior experience needed. You might be wondering about the name “p5.js”. It’s a JavaScript library (that’s the “.js” part) based on Processing, the open-source programming language created for artists and designers. (More on Processing in a moment.) I have taught college courses with this book, and students love it. Plus, all the skills you learn here with p5 are applicable to JavaScript—the world’s most popular programming language—more generally.
Processing opened up the world of programming to artists, designers, educators, and beginners. The p5.js JavaScript implementation of Processing reinterprets it for today's web. This short book gently introduces the core concepts of computer programming and working with Processing. Written by the co-founders of the Processing project, Reas and Fry, along with Lauren McCarthy, one of the minds behind p5.js, Getting Started with Processing gets you in on the fun!
I’ve always loved technology. I like the constant change, the sense of creativity and invention, of how it can act as an incredible force for good and human progress and betterment in the world. I can’t remember a time when I wasn’t tinkering with gadgets—taking radios apart to mend them or learn how they worked; designing electronic circuits for music synthesis; programming computers. But I’ve also always been interested in politics and the complex intersection of technology and public policy. So much so that most of my working life has been spent at this intersection, which is why I love these books—and hope you will too.
Virginia Eubanks writes in an incredibly immersive and engaging style, making her book as compulsive as a work of fiction—and equally hard to put down. It exposes the deeply toxic consequences of the way automated decision-making increasingly dominates our public institutions, creating a sort of “twenty-first century digital poorhouse”.
This automated inequality denies citizens their humanity and any sense of agency, condemning them to the sort of negative moral judgments and cycle of decline and despair that would have been familiar to Charles Dickens in his day.
In Indiana, one million people lose their healthcare, food stamps, and cash benefits in three years-because a new computer system interprets any application mistake as "failure to cooperate." In Los Angeles, an algorithm calculates the comparative vulnerability of tens of thousands of homeless people in order to prioritize them for a shrinking pool of housing resources. In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect.
Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change.…
Like
professionals in other industries, educators are recognizing the power of data
and are using it to guide their decision-making. Yet quantifying what works and
what doesn’t when it comes to something as variable-rich as learning is
extremely difficult.
Hence, as bright as they are, educators have only a 14%
accuracy rate when interpreting student data. Fortunately, authors Rees and
Wynns have exactly what is needed to remedy this problem.
They offer the
hard-but-important-to-look-at facts concerning data use in our schools and pair
it with a clear path to fixing problems. They pull engaging stories from their
extensive experience and have a whip-smart writing style I envy.
One wonders
how a book on data that uncovers harsh realities can be such an enjoyable read –
the kind too enthralling to put down.
This book helps school and district leaders avoid the pitfalls that await those making sense of their school's data. Whether you're interpreting achievement gaps, graduation rates or test results, you're at risk of reaching a mistaken judgment. By learning about common errors and how they're made, you'll be ready to choose safer, surer paths to making better sense of the wealth of data in your school or district. The authors help educators build better evidence, see conclusions more clearly, and explain the data more persuasively.
Special features Include:
"Questions to Spark Discussion" in each chapter encourage school site, district leaders,…
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, logistic…
As a child my heroes were designers and I thought designers could design across many disciplines, this was what I understood and aspired to. I'm fortunate to have been a designer, illustrator, and design teacher for many years. Passionate about the process I firmly believe if you can design in one area you can design in another. Understanding your material's potential is the key. As a tutor and author my job is to unwrap a student’s talent, support and encourage that unique view through skills building and advice to help them. I believe good design can solve many of the world’s problems and passing on that message is valuable.
Ching has a great gift for illustrating with his visuals, and his amazing handwritten text, all manner of information about drawing and designing space. This is a comprehensive and instructional book introducing design drawing from basic principles to the communication of designed space as a structural diagram or atmospheric perspective. A wonderful exploration of sketching and drawing methods to illustrate theory, atmosphere, and the communication of three-dimensional space. For me, it transcended the textbook approach and provided a clear exploration of the communication of design method and its potential outcomes.
THE CLASSIC GUIDE TO DRAWING FOR DESIGNERS, REVISED AND UPDATED TO INCLUDE CURRENT DIGITAL-DRAWING TECHNIQUES
Hand drawing is an integral part of the design process and central to the architecture profession. An architect's precise interpretation and freedom of expression are captured through hand drawing, and it is perhaps the most fundamental skill that the designer must develop in order to communicate thoughts and ideas effectively. In his distinctive style, world-renowned author Francis D. K. Ching presents Design Drawing, Third Edition, the classic guide to hand drawing that clearly demonstrates how to use drawing as a practical tool for formulating and…
For the last 25 years, I have been a coach to business founders, leaders, and leadership teams. My work has taken me to every continent from my base in London. A lot of my work is done behind closed doors, but I have been instrumental in building two unicorns in the last decade. I’m a founder myself and have always been fascinated by what it takes to succeed as a founder. I have a powerful conviction that learning to lead is the heart of it. The books I love are either based on real-world research or deeply practical and based on hands-on experience. Practice trumps theory every time in my world!
I love the fact that Ali has provided evidence that ANYONE can succeed as a founder. He has done a lot of number-crunching on ‘unicorns’ (I admire his tenacity for that!) and examined what makes a successful startup founder and some surprises emerge.
Age, education, and number of cofounders were not predictors of a startup’s success, as many of us might have expected. The big thing that does matter is experience. Sixty percent of unicorn founders had previously launched startups. It seems that in pretty much every walk of life, including this one, practice is the key.
Every VC wants to find the next billion dollar company to invest in, and every startup wants to become one. Ali Tamaseb set out to find patterns in the backgrounds, methods, and trajectories of these companies, gathering and analyzing 40,000 data points about the 200+ billion dollar companies and the people who founded them. And you'll be surprised by what he discovered:
* Half of unicorn founders are over 35; * Most founders don't have any directly relevant work experience in the industry they're disrupting; * There's no disadvantage to being a solo founder; * Sixty percent of billion dollar…
