I believe that for those new to nuclear engineering, Introduction to Nuclear Engineering by Anthony J. Baratta and John R. Lamarsh is probably the more well-known book. I also started my studies with that book. However, I think it can be somewhat difficult for self-study. I personally attempted to study it on my own twice but ended up giving up midway.

That's why I would rather recommend Fundamentals of Nuclear Reactor Physics by E.E. Lewis. Above all, it is easier to read and includes a reasonable number of practice problems, making it great for self-study. Additionally, if you ever struggle to recall a formula or theory, this book covers most of what you need, making it an excellent reference. Having it on your bookshelf gives you a great sense of reassurance.

When I was doing my master's, I studied nuclear physics and conducted research on micro-radiation detection. At that time, my master's advisor highly recommended this book to me, telling me to read it carefully. It has already been 10 years since I first encountered this book.

Of course, during my master's studies, I lacked a strong background in statistics and mathematics, so even reading just the necessary sections took me a tremendous amount of time. In fact, my master's thesis was essentially just applying a portion of this book to actual experimental data.

As time passed and I started my PhD, one of the first courses I took was a radiation experiment class. This book was once again one of the reference texts for the course. I was happy to see it again, and wanting to do well in the class, I had the opportunity to study it more deeply. This book is like the Bible of the radiation field.

This book has significantly expanded both the depth and breadth of my knowledge in neutronics, allowing me to confidently say that my major is reactor physics. Although it was written in 1976 and is quite old, most of the fundamental physics in nuclear engineering was established in the 1950s. Therefore, it is fair to say that this book covers nearly all aspects of modern neutron theory. 

Moreover, it not only addresses nuclear engineering problems from a physics-based perspective but also explores numerical methods for solving them. While it is somewhat challenging, mastering this single book will ensure that you are well-versed in reactor physics and competitive in the field. I highly recommend it to those who wish to study reactor physics in depth and take a step further toward becoming an expert in nuclear engineering.

This book can be considered an encyclopedia of statistics, covering everything from the very basics to advanced topics. It provides a comprehensive and structured approach to understanding probability theory and statistical analysis, making it an essential resource for both beginners and experienced practitioners.

Whether you are new to probability and statistics or looking to deepen your expertise, this book serves as an excellent reference and learning tool. Its systematic approach and well-structured content make it particularly valuable for engineers, scientists, and researchers who need to apply statistical techniques in their work.

This book is perfect for those who need to learn the fundamentals of statistics for data science but do not necessarily have to study traditional statistics in its entirety. It was written by a professor whose class I took during my Ph.D. studies at Purdue. That course was one of the best I had at Purdue, and I could tell that he poured his passion into writing this book.

Before, I saw machine learning and artificial intelligence as black boxes—complex systems that processed data in ways I couldn't fully understand. However, after reading this book and attending Professor Stanley H. Chan's lectures, I realized that these technologies were not black boxes at all, but rather powerful tools that I could wield with confidence.

Even now, I often refer to this book when I encounter difficult problems, and it continues to inspire me every time I open it.

This is a book I truly love, but unfortunately, I haven’t finished it yet. During my Ph.D., I kept postponing it, thinking it wasn’t directly related to my research. However, I’m finally studying it now. It covers a wide range of topics essential for data science. Instead of focusing on fundamental mathematics and statistics, it dives straight into data, providing numerous examples to aid understanding.

One of the biggest advantages of this book is that its lectures are available on YouTube. Both Professors Steven L. Brunton and J. Nathan Kutz are exceptional instructors. If you get stuck while reading, you can refer to the video explanations, which is an incredible resource.

If you're looking to quickly build foundational knowledge in data science that you can apply to research right away, I believe this book is an absolute must.

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