The best data processing books

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.

I fell in love with technology when I wrote my first computer program at age 14 when there was no public Internet, no personal computers, no iPhone, no cloud. I have made technical contributions to every era of computing from mainframes, to PCs, Internet, Cloud, and now AI. I was recently elected to the National Academy of Engineering. AI currently surpasses my wildest imagination on the art of what’s possible. I'm still passionately working in technology at Google focused on how to live healthier lives. I believe we can make AI the telescope of the future, to helping everyone live long and healthy lives.

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.

I have read most of the books written about Apollo, especially those ostensibly written by my fellow participants. I have read these books for pleasure, to find out about parts of the moon effort that I did not see first-hand, and to learn what I could from the authors’ mistakes and successes — with a view to the writing of my own book. The books I have come to value the most are the books that seem to have been created for some other reason than commercial gain, the books unmarred by ghostwriting or heavy-handed editing, the books where the author’s authentic voice speaks from the page.

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.

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

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 get visually interesting and 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!

A noted quantitative hedge fund manager and quant finance author, Ernie is the founder of QTS Capital Management and Predictnow.ai. Previously he has applied his expertise in machine learning at IBM T.J. Watson Research Center’s Human Language Technologies group, at Morgan Stanley’s Data Mining and Artificial Intelligence Group, and at Credit Suisse’s Horizon Trading Group. Ernie was quoted by Bloomberg, the Wall Street Journal, New York Times, Forbes, and the CIO magazine, and interviewed on CNBC’s Closing Bell program. He is an adjunct faculty at Northwestern University’s Master’s in Data Science program and supervises student theses there. Ernie holds a Ph.D. in theoretical physics from Cornell University.

I have been building real-time, production machine learning models for over 20 years. My book, and my book recommendations, are informed by that experience. I have a lot of empathy for people who are new to machine learning because I’ve taught courses on the topic. I founded the Advanced Solutions Lab at Google where we helped data scientists working for Google Cloud customers (who already knew ML) become ML engineers capable of building reliable ML models. The first two are the books I’d recommend today to newcomers and the last three to folks attending the ASL. 

I moved into content design from a career in brand and marketing, at a time when the discipline was emerging and not many people really knew what it was. Much of my time since has been spent educating people and organisations and sharing knowledge to help them make better content decisions. Throughout this time, I’ve learnt most of what I know through the experience of working with the design teams, but so many books have also helped me along the way and made my work so much better. I love content design – having the power to improve people's experiences with brands through words is so rewarding, and these books will inspire others to do the same.

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 get visually interesting and 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!

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.

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.

I am a scientist and technologist, trained in theoretical quantum physics, who became an Emeritus Professor of Network Technology from Oslo’s metropolitan university. I’ve strenuously tried to communicate the wonder of science to students and industry throughout my career. I’m also a long-standing fan of science fiction who grew up with heroes in both fact and fiction. The idea of future society has haunted me my whole life. I’m an optimist, who looks to the darker tales as warnings of futures we hope to avoid. Read these tales with a determination for us all to do better.

Rupert Younger is an author and entrepreneur. He is the co-author of The Reputation Game a bestselling book published in October 2017 (with David Waller) now published in six languages, and co-author of The Activist Manifesto (with Frank Partnoy), a reimagining of what Karl Marx and Friedrich Engels would have written had they been alive today. His work and views are regularly featured in major news outlets including the BBC, CNN, the Financial Times, The Wall Street Journal, and The Times of London. Rupert is the founding director of Oxford University’s Centre for Corporate Reputation, a leading research centre focused on social evaluations, and co-founder of the global strategic consulting firm Finsbury Glover Hering.

I fell in love with technology when I wrote my first computer program at age 14 when there was no public Internet, no personal computers, no iPhone, no cloud. I have made technical contributions to every era of computing from mainframes, to PCs, Internet, Cloud, and now AI. I was recently elected to the National Academy of Engineering. AI currently surpasses my wildest imagination on the art of what’s possible. I'm still passionately working in technology at Google focused on how to live healthier lives. I believe we can make AI the telescope of the future, to helping everyone live long and healthy lives.

I am motivated by working on products that many people use. I've been a part of companies that deliver products impacting millions of people. To achieve it, I am working in the Big Data ecosystem and striving to simplify it by contributing to Dremio's Data LakeHouse solution. I worked on projects using Spark, HDFS, Cassandra, and Kafka technologies. I have been working in the software engineering industry for ten years now, and I've tried to share my experience and lessons learned in the Software Mistakes and Tradeoffs book, hoping that it will allow current and the next generation of engineers to create better software, leading to more happy users.

It’s been fantastic to work in computer vision, especially when it is used to build biometric systems. I and my 80 odd PhD students have pioneered systems that recognise people by the way they walk, by their ears, and many other new things too. To build the systems, we needed computer vision techniques and architectures, both of which work with complex real-world imagery. That’s what computer vision gives you: a capability to ‘see’ using a computer. I think we can still go a lot further: to give blind people sight, to enable better invasive surgery, to autonomise more of our industrial society, and to give us capabilities we never knew we’d have.

I am an economist who came to realize that the marketplace of ideas was a political doctrine, and not an empirical description of how we came to know what we think we know. Science has never functioned in the same manner across centuries; it was only during my lifetime that it became recast as a subset of market reality. I have spent a fair amount of effort exploring how economics sought to attain the status of a science; but now the tables have turned. It is now scientists who are trained to become first and foremost market actors, finally elevating the political dominance of the economists.