Links to many data sets for teaching and research in statistics. This book should be of interest to statistics lecturers who want ready-made data sets complete with notes for teaching. If someone could provide alink for below book, I would be grateful Handbook of Corrosion Data edited by Bruce D. Thanks for your time. Handbook of Research on Fuzzy Information Processing in Databases (2 Volumes): 536: Computer Science & IT Books.
Who is this Book For?
Objectives
The aim of this book is to help people to become better visual communicators of data through the optimisation of creative, analytical and contextual decision-making. The specific purpose of the second edition is to take the opportunity to make desired edits to the contents that reflect how my own convictions have evolved, and the way I communicate them, in the years that have passed since completing the first edition. Unless you are a committed completist collector of books, if you already have the first edition you may not need to also invest in this new edition, but certainly if you are buying this text for the first time this is the edition to go for.
The primary challenge one faces when writing a book about data visualisation is to determine what to leave in and what to leave out. Data visualisation is big. It is too big a subject to even attempt to cover it all, in detail, in one book. There is no single book to rule them all because there is no one book that can cover it all. Each and every one of the topics covered by the chapters in this book could, and indeed do, exist as whole books in their own right.
The secondary challenge is to decide how to weave all the content together. Data visualisation is not rocket science; it is not an especially complicated discipline: lots of it is rooted in common sense. It is, however, certainly a complex subject. There are lots of things to think about and decide on, as well as many things to do and make. Creative and analytical sensibilities blend with artistic and scientific judgments. In one moment you might be checking the statistical rigour of your calculations, in the next deciding which tone of orange most elegantly contrasts with an 80% black. The complexity of data visualisation manifests itself through how these different ingredients, and many more, interact, influence and intersect to form the whole.
A Handbook Of Small Data Sets Book
I have arrived at what I believe to be an effective and proven pedagogy that successfully translates the complexities of this subject into accessible, practical and valuable form. I feel well qualified to bridge the gap between the large population of everyday practitioners, who might identify themselves as beginners, and the superstar technical, creative and academic minds that are constantly pushing forward our understanding of the potential of data visualisation. I am not going to claim to belong to that latter cohort, but I have certainly been the former – a beginner – and most of my working hours are spent helping other beginners start their journey. I know the things that I would have valued when I was starting out and I know how I would have wished them to be articulated and presented to me.
There is a large and growing library of fantastic books offering many different theoretical and practical viewpoints on the subject of data visualisation. My aim is to add value to this existing collection of work by taking on a particular perspective that is perhaps under-represented in other texts – exploring the notion and practice of a visualisation design process. It is my belief that the path to mastering data visualisation is achieved by making better decisions: effective choices, efficiently made. The aim of the book is therefore to help readers elegantly navigate through the process of what things to think about, when to think about them, what options exist and how to make the best choices.
Just as a single book cannot cover the whole of this subject, it stands that a single book cannot aim to address directly the needs of all people doing data visualisation. Here are some of the characteristics that shape the readers to whom this book is primarily targeted. This will help manage your expectations as a potential reader and establish its value proposition compared with other titles:
Domain, duties and level
The core audiences for whom this book has been primarily written are undergraduate and postgraduate-level students and early career researchers from social science subjects. This reflects a growing number of people in higher education who are interested in and need to learn about data visualisation. Although aimed at social sciences, the content will also be relevant across the spectrum of academic disciplines, from the arts and humanities right through to the formal and natural sciences: any academic duty where there is an emphasis on the use of quantitative and qualitative methods in studies will require an appreciation of good data visualisation practices. Where statistical capabilities are relevant so too is data visualisation.
Beyond academia, data visualisation is a discipline that has reached mainstream consciousness with an increasing number of professionals and organisations, across all industry types and sizes, recognising the importance of doing it well for both internal and external benefit. You might be a market researcher, a librarian or a data analyst looking to enhance your visual communication capabilities. Perhaps you are a skilled graphic designer or web developer looking to take your portfolio of work into a more knowledge of the subject.
Contemporary vs Historical
The subject matter, the ideas and the practices presented here will hopefully not date a great deal. Of course, many of the graphic examples included in the book will be surpassed by newer work demonstrating similar concepts as the field continues to develop. However, their worth as exhibits of a particular perspective covered in the text should prove timeless. As more research is conducted in the subject, without question there will be new techniques, new concepts, new empirically evidenced principles that emerge. There will be new thought-leaders, new sources of reference, new visualisers to draw insight from. New tools will be created, existing tools will expire. Some things that are done and can only be done by hand as of today may become seamlessly automated in the near future. That is simply the nature of a fast-growing field. This book can only ever be a line in the sand.
Looking back, we all respect the ancestors of this field, the great names who, despite primitive means, pioneered new concepts in the visual display of statistics to shape the foundations of the field being practised today. The field’s lineage is decorated by names and classic examples that frequent most books that have been written to date about this subject. Of course, to many beginners in the field, this historical context is of huge interest. However, again, this kind of content has already been superbly covered by other texts on more than enough occasions. Time to move on.
A Handbook Of Small Data Sets Free
Analysis vs Communication
A Handbook Of Small Data Sets
A final important distinction to make concerns the subtle but significant difference between visualisations which are used for exploratory analysis and visualisations used for communication. Exploratory analysis is a huge and specialist subject in and of itself. In its most advanced form, working efficiently and effectively with large complex data, topics like ‘machine learning’ become increasingly relevant. For the scope of this book the content is weighted more towards methods and concerns about communicating data visually to others. That said, Chapter 4 will cover the essential elements of the approaches to exploratory analysis in sufficient depth for the practical needs of most people working with data.