Data science is a fascinating field and luckily there are tons of excellent reading materials
out there to help you master it. I notice that some of them
are not very well known and I don't see other
people recommending them on their channels or elsewhere. I feel that I'm doing
you guys a disservice if I don't mention them
here on my channel. These books have been absolute
game changers for me not just for learning new
concepts, but also for preparing for interviews and solving real
world data science problems. Whether you're just starting out or you're a seasoned data scientist you will find these
books incredibly useful. In this video, I'm excited to share with you some of my
all-time favorite books for learning and practicing data science. Since data science is such a vast field I'm narrowing my recommendations down to two specific areas,
statistics and machine learning. So without further ado, let's dive in. First up, let's talk about statistics. If you are new to the
field or want to brush up on your knowledge, I've
got a recommendation for you. Open Intro Statistics. It's a great beginner
friendly textbook that covers all the essential
concepts that every data scientist should know, including
inference and regression. Now, I know this book looks pretty huge but don't let that intimidate you. It's actually pretty easy
to read and understand especially if you've taken
any introductory level statistics courses before. The authors have done a great job of making the content
engaging and accessible. Plus, there are plenty of helpful diagrams to help
you visualize complex ideas. This book is perfect for
beginners who want to get a solid foundation in statistics and even if you are already
working in the field it's a great resource to have on hand for refreshing your statistics knowledge. The book covers everything
from axioms of probability to distributions, hypothesis
testing and regression so there's something for everyone. The best part? You don't need to sit down and read it all at once. You can pick it up during your downtime and read a few pages at a time. It's not overly technical
or difficult to read so you can easily fit it
into your busy schedule. I actually review this
book every few months to refresh my memory on statistics. It's that good, and the cherry on top You can download the ebook
for free at www.openintro.org. I have also included the
link in the video description. So what are you waiting for? Go check it out. All right, so you've got
the fundamentals down and now it's time to dive a little deeper. The next book I want to
recommend is this one. Mathematical Statistics
with Resampling and R by Laura Chihara and Tim Hesterberg. I've got the first edition but the latest one is the second edition and the cover looks a little different. It looks like this. This book is a comprehensive
textbook that takes an intermediate level approach
to exploring the concepts of mathematical statistics
using the resampling method and the R programming language. It covers a range of topics
including probability theory hypothesis testing, confidence intervals, and linear regression among others. What sets this book apart from
the previous one we talked about is that it's more
in depth and practical. In fact, it's actually a textbook
for undergrad stats majors. The authors do an excellent job of explaining resampling methods like bootstrapping and permutation tests and they use the recsampling
method extensively throughout the book to
provide a practical approach to statistical analysis. The book also includes
plenty of examples, exercises, and R code to help readers
develop a deeper understanding of the concepts. It's an excellent resource for data science professionals who want to expand their statistics knowledge
and learn practical skills. Before I read this book, I
had heard of resampling methods but I didn't fully understand
them or know how to use them but after reading this book,
everything clicked into place and I was able to start using
those methods in my own work. By the way, I have to
give credit to my friend Yuan for recommending both of
these statistics books to me. Yuan is one of the smartest
data scientists I know and currently works at DoorDash as a machine learning data scientist. So if they say these
books are good, you know they've got to be good. Now let's move on to machine learning. The first book I recommend
is The Hundred Page Machine Learning Book by Andriy Burkov. This book is an excellent starting point for anyone who wants to
learn machine learning. It provides a clear and concise summary of the key concepts and ideas,
which is really important if you want to understand
more advanced topics later on. One of the things I
love about this book is that it's only a little
over a hundred pages long so you can read it in
just one day, and even if you already read other
books on machine learning this one can still be a great refresher especially if you are
preparing for interviews. What I think sets this book apart is how it pieces things together. Instead of looking at
all the different machine learning algorithms individually, it talks about the similarities and
differences between them so you can learn how they relate to each other and better understand them. For example the book does a fantastic job
summarizing the three parts of a machine learning
algorithm: the loss function the optimization criterion,
and the optimization routine. By understanding these three things you can organize your knowledge and learn new algorithms
more easily, and you can look at all algorithms through the
lenses of these three things. As an added bonus, you
can actually download some of the books chapters for free and read them before deciding
whether to buy the book. So if you like what you read and find it useful in your work or studies then you can go ahead and buy the book. You can check out the website themlbook.com to learn more. Another great find on machine
learning is Machine Learning with PyTorch and Scikit-Learn which is a comprehensive
guide that I use to expand my knowledge in this field. This book is written by experts in the field
of machine learning. This book covers
everything you need to know about using Pytorch and Scikit-Learn for
machine learning. Starting from the basics of machine learning. this book guides you through the installation and setup of PyTorch and Scikit-Learn. One of the things I really appreciated about this book was its
emphasis on implementation. The step-by-step implementations
of various algorithms such as principle component analysis and Gaussian mixture model
are particularly helpful in understanding what's happening under the hood. With exercises
and coding examples throughout the book, you will have plenty of opportunities to practice and apply what you have
learned, but that's not all. The authors provide clear
explanations of the concepts and algorithms, and they
use real world examples to show how these tools can
be applied in practice. They also take you through some
of the more advanced topics in machine learning, including
deep learning, convolutional neural networks, and
natural language processing. Overall, this book is an amazing resource for anyone who wants to learn how to use these powerful Python
libraries for machine learning. As someone who has used this
book to expand my knowledge in machine learning, I can
say that is well written, easy to follow, and it covers
a wide range of topics. So if you are looking to up
your machine learning skills I highly recommend checking out this book. So once you've picked up the
machine learning fundamentals I have a more practical
book I'd love to recommend and that is Designing
Machine Learning Systems An Iterative Process for Production Ready
Applications by Chip Huyen. I hope I pronounced that correctly. What makes this book so
great is that it's not just for data scientists and
machine learning engineers it's for anyone who wants to become one. The book provides a holistic view of complex machine learning systems. So you will learn not just
how to scope a machine learning project, but also how
to process data, debug models, and productionize them,
and let me tell you building machine learning
models is only a small part of the job. That's why this book does
not even cover any machine learning algorithms in detail. By reading this book, you
will get a clear understanding of what it really means
to be a machine learning practitioner. You will learn
how to navigate the day-to-day work involved in processing
data, debugging models, and productionizing them. It's really eye-opening and
it will help you make more informed decisions about your career. And if you are already a
data science or machine learning practitioner, this
book can help expand your knowledge in other
areas of the system such as data engineering, model
deployment, and ML ops. One of the key takeaways for
me is that what your employers ultimately care about is
business metrics, much more than fancy machine learning metrics. So it's always important to
clearly communicate the business impact of your work in the
real world, but that's not all. This book also comes
with a GitHub repository that contains practical
blog posts on the most up to date best practice
information available in the industry today, so
you can continue learning and staying up to date even
after you finish reading the book. So whether you are just
starting out in machine learning or you're a seasoned
practitioner, I highly recommend checking out Designing
Machine Learning Systems. You won't regret it and don't forget to check
out the link in the video description for more
information. That's all for today's video on my
favorite data science books for learning and practicing
statistics and machine learning. I hope you found this
information valuable and that it will help you advance
your data science skills. If you enjoy this video and want to see more content
like this, be sure to subscribe to my channel and hit
the notification bell to stay up to date on all my latest content. Don't forget to give
this video a thumbs up and leave a comment letting me know which of these books you're
most excited to read. Thanks for tuning in and I
will see you in the next video.
