Great for students or experienced researchers
584680
HCI Remixed provides an interesting history of the young field of human-computer interaction (HCI) through reflections on high-impact papers in the field.  Each chapter is a brief 4-6 page essay by a single current researcher or practitioner in the field of HCI, reflecting on a paper that they found important.  The book's 51 divided into seven loosely-defined sections.  The two editors provide a brief introduction to each of the seven sections.

I'd read many of the original papers before, but this book provided the opportunity to see how the papers impacted active researchers in the field--often in surprising and deeply personal ways.  And for papers I hadn't read, the essay provided a great introduction to a topic.  I've used this for multiple groups of undergrad researchers and I've recommended chapters to grad students and other researchers.  It seems to work best for people who have (or want) an understanding of the history of HCI from some of the top people in the field.  It's not necessarily a great first book for the field, but it provides a great and highly personal reflection of some influential ideas in HCI.
2587447
This is a fabulous book that offers an overview of many female scientists. It starts with ancient scientists around 2700 BCE and goes through the 20th century. It tells about women in science in four chapters:  Explorers of Science, Engineers and Inventors, Computer Scientists, and Super Scientists. Each chapter has photographs, illustrations, and information about important women who have blazed a trail in scientific fields. The end of the book shows Nobel Prize winners, has an infographic about careers in science and a 12 question quiz covering material from the book. There is also a short glossary and index. It's a Level 3 book for those who can read on their own and recommended for ages 7 to 9. I think younger readers would enjoy it too, they just may need help with some of the names and more challenging words. It's a great book for a classroom library.
1561546
I bought this for a writing class I am required to take before I graduate.  It is a great resource.  I remember buying these when I was just a little freshman and they were never of much worth to me.  This one is spiral bound, so it lays flat, it has sample papers to show you exactly how to use the format you need and many exercises that I have admittedly never touched.  It will be a good resource for undergrads, graduate students or anyone who is interested in writing a research paper.  It also has ample information on how to document electronic sources, which most guides tend to skimp on.
2125363
I like this text.  It's great reference and easy to navigate.  Not for casual Sunday reading, but perfect when you need to review how to do standard addition analysis using excel, or how to calculate standard deviations with activity coefficients.  In many ways, the book is the essential review of data analysis for most gen chem and organic applications.  As such, it is useful for the advanced student who needs to review the basics while still offering the advanced applications and concepts that the professional may need but doesn't have memorized (like a big cheat sheet that doesn't seem as awkward as keeping more basic texts on your bookshelf).  I'm in med school now and use the text to make sure I have done appropriate data analysis and described the basic science properly in my research reports.
3743421
Meier takes you as far as you can go into historical Jesus research without knowing Greek, Hebrew and Aramaic.  The book summarizes and evaluates the current state of research using a well-defined methodology and comes to its own conclusions. It's not easy to read: footnotes are about equal to text, and the print is uncomfortably small, but if you really want to know as much as you can about Jesus, you need this book.
1600134
I purchased and read this book because I was interested in learning about what data scientists "do" on a day-to-day basis, and the book definitely delivers in that respect.  It is very up-front in stating that it does not cover many of the details (e.g., requisite math/statistics background, coding in R, etc.), but it provides numerous references for learning this material for those who wish to take a deeper dive.  It provides perspectives from a number of experts in the field, along with their associated domains of expertise, and hence appears to give a good sampling of the different backgrounds and personalities of self-labelled data scientists.

From a pedagogical perspective, I think it's a valiant attempt to create a class that's not so "academic."  The book is a compendium of individual lectures that were the basis of a data science class at Columbia University, and the corresponding assignments were aimed at giving students a flavor of real-world data science problems (where data is messy, specific questions regarding outcomes are not-well-formed, etc.), which I think is an amazingly valuable experience to give students perspective on what the field is about.  But I wonder if the course went a little *too* far into "the real world is not pretty" aspect of things.  The students wrote a chapter at the end of the book where they criticize a standard academic problem that comes from a statistical learning textbook and that, in my opinion, is a negative outcome.  While elegant mathematical theories do not describe the complexities of the real world, understanding the subtleties of algorithms is an important part of any scientific field, and to discount that is a disservice to the students.  Too much depth and not enough breadth is bad in the real world, but so is not having *enough* depth.  On the whole, though, I really applaud the approach of the authors at building a "real-world" class.

One of the things that book has left me pondering is whether there is a clear distinction between data *science* and data *engineering*, and whether it is possible to have clear roles for people who are predominately scientists than those who are predominately engineers.  A few of the guest lecturers claim that 90% of the work of a data scientist is organizing the data, but I can't help but wonder if that's the bias of the individual speakers---that they *like* this component of the work and, therefore, focus on that more, rather than it being an absolute must to spend 90% of your time working on data structuring.  I am sure that if the authors were to read this comment, they would think that I am missing one of their major points (namely, that one can make a lot of mistakes in data leakage and that like from poor data structures, hence making this an integral part of the process).  But I think there's a certain personality type that really loves working on the actually processing component who would otherwise be turned off my too much data structuring if it really composed 90% of the work.  And harnessing the skills from those types of people is undoubtedly invaluable.  So I'm really wondering if many companies are able to break up roles where some people (the scientists) focus more on the processing part, while others (the engineers) focus more on the structuring part.
1469327
Used this book for a graduate course in statistics in which we used the R software. I love how clear and concise each chapter is and at the end includes a few examples that were previously shown "by hand" earlier in the chapter, but now show how to perform it in R. I found the end of chapter problems to be progressive in difficultly with the first few problems similar to examples in the book, and the later problems testing understanding of material and application. One thing I found that was unique is the book introduces several examples that we first encounter in the first chapter then as the chapters go on, we revisit the same examples, adding onto them with the knowledge gained. I found this to be very beneficial to see the same data being built upon and shown the evolution of answering the example basic then building up to analyzing the data with advanced statistical methods.

Some of my classmates found the book to be difficult, i think some understanding of basic statistics is necessary since the book assumes and doesn't go into great depth on some basic concepts. But having taking a statistics course previously, I had no issue following along and plan to keep this book as reference instead of trying to sell it, that is how much I love this book!
3325959
I thought this was exceptionally well done. I was already familiar with much of the research covered in the book but it is very helpful to have this material summarized in such readable, non-technical language. I will consider using this in one of the courses I teach. A few additional thoughts are available here: http://www.fullerinstitute.org/?p=811
719651
Have you ever faced a blank computer screen and were at a complete loss of what you should write about for a 10-page research paper due the next week? Or maybe you knew what you wanted to write about but didn't know how to start? Or maybe you had all your sources, wrote out a draft and realized that no one cares if The Great Gatsby illustrates the three Aristotelian elements of a tragedy?

The Craft of Research helps students and researchers solve dilemmas like these and more. The authors dissect the anatomy of a research paper and create step-by-step stages that guide you all the way from choosing a topic to polishing your final product.

The major sections of this book address how to form a good research claim that your readers will care about; how to find and evaluate sources; how to support your claim with evidence, reasons and warrants; and how to prepare, draft and revise your paper. The authors use simple and clear language, and if that's not enough, they provide easy-to-understand visuals and diagrams to help make their point.

The authors also cover useful areas such as ethics (why you must always cite even when just discussing an idea of another writer's), the Internet (when it's acceptable to use web-based sources), and visuals (why 3-D graphs are a bad idea).

Sure, some of the advice they provide you may already know, but as the authors cover nearly everything to do with research papers (albeit in a generalized way), there's something for everyone. It's also nice to have a guide that will remind you of everything you learned in your freshman English classes. Clear, concise, and accessible, the Craft of Research is one of the best books on research.
2413589
I used this text extensively for my college thesis on white shark evolution and it is excellent. I recommend it to any academics looking for a thorough resource and any amateur shark lovers who want to know more details about these wonderful animals.

The chapter of white shark evolutionary relationships is older and not in line with some of the latest paleontological relationships drawing a direct line of descent between Isurus (recently reassigned Charcharodon) hastalis and Charcharodon charcharias but it is always good to see both sides of a debate and the rest of the information is great.
615283
As a college professor, this book is fantastic. I had been doing many of these things already. This book gave me the research to help make learning processes even better for students.
2679636
I just bought this book, in spite of the previous review, and found it useful and informative.  I was surprised by the depth of the information.
If you are working on any piece that requires exhaustive research I'm  sure you would want to check more than one source.  But this book is a  wonderful place to begin your research.
109822
wonderful research book , amazing information.  Photos are outstanding.  Highly recommend this book especially to students. It opens a whole new world of knowledge.
4330226
I liked this volume very much.  The article that really stood out for me (because of my research interests) was the one on use of anonymous student evaluations.  I found that one article was almost worth the price of the book.  It was balanced and informative.  It is well-documented and very readable.  I thought it stopped short of discussing the inherent unreliability of such anonymous evaluations and could have done a better job of explaining the various ways in which faculty can sometimes "game" the system and the ways in which factors such as class size and the shape of the classroom, factors outside any faculty member's control, influence student evaluations.  As I write this, I tried to find my copy and discovered that it is out on loan to a junior faculty member who is facing having to use student evaluations for her tenure decision.  [Usually I do not loan my books - but I saw the need was there.]
2652773
Anthony Davison is one of the rare applied statisticians with in-depth knowledge in statistical theory. He is an excellent researcher and lecturer. Anthony is the editor of Biometrika, one of the most prestigious methodological journals in statistics, for couple of years.
Take a look at the content of the book and you will see that it covers almost everything that a researcher requires to know about statistics. I would summarize this book as what is useful in statistics to learn, with focus on methodology.  This book is difficult to use as a textbook, and much more suitable as a reference. I can even call it the bible of statistics. The fundamental ideas in statistics are clearly explained, and argued with excellent motivating examples. Exercises are even better and completes the lecture, but they are not trivial at all. Some of the exercises are simpler versions of a research problem, being borrowed from a research article. You need some knowledge of mathematics in the level of calculus-based probability and statistics to start reading this text. After finishing the book you will have a general picture of what is statistics.
1457497
This guide is excellent in providing a step by step process in understanding complex statistical functions and tests for research. It is easy to read and understand, and makes research fun! Even though its directed towards SPSS, I always refer to the guide to clarify my statistical doubts and to guide me further in research planning/process/methodology. Must buy for students, researchers and teachers/lecturers.
4880279
This book is a great resource for working with groups tasked with projects in an academic setting.  It is geared toward somewhat larger groups (8-15) than the teams I tend to work with (most of my team projects are 4-6 students), but the book looks like it will be enormously helpful and I plan to implement many of these ideas in the upcoming semester.  I found it encouraging the the authors mention that some great groups get off to a rocky start (something I spend a lot of time worrying about), and provides insight into why groups fall apart and how to prevent and fix these issues.