You may doubt if it is worth spending time on a book when everything can be found online these days. Practicum experts say “yes”. Books provide in-depth coverage of topics and case studies that can help tech specialists think more creatively and develop more effective solutions to complex problems.

Here is a list of books approved by Practicum experts. Save it to bookmarks, add it to your wishlists, but most importantly - read up!

Data Science From Scratch: First Principles with Python by Joel Grus

Recommended by: Daniel DeFoe, DS tutor, Mid-Level Software Developer at Whitespace

Summary: The book covers a broad range of topics, from statistics and probability theory to machine learning and data visualization, all using the Python programming language. Grus begins with the basics of Python programming, providing readers with a solid foundation in the language before diving into the more complex topics. He then introduces statistical concepts such as probability distributions, hypothesis testing, and correlation analysis and shows how these can be used to gain insights from data. The book also covers machine learning techniques such as decision trees, clustering, and neural networks.

Why you should read it:  What sets Data Science From Scratch apart is its focus on practical applications. Grus provides code examples and exercises that allow readers to apply the concepts they have learned to real datasets. He also includes tips and tricks for working with messy data and dealing with common pitfalls.

CODE: The Hidden Language of Computer Hardware and Software by Charles Petzold

Recommended by: Travis, Editor at Practicum

Summary: "CODE” explores the history and inner workings of computers. The book takes a bottom-up approach, starting with the basics of electrical circuits and building up to the high-level programming languages used today. Throughout the book, Petzold uses clear and accessible language to explain complex concepts in a way that is easy to understand. 

The book also includes practical examples and exercises to help readers apply what they've learned.

Why you should read it: It provides a fascinating insight into the world of computing and explains the fundamental principles that underpin the digital age. Petzold takes complex concepts and presents them in a way that is easy to understand, without oversimplifying the material. He also includes numerous diagrams and illustrations to help readers visualize abstract concepts.

“It is a great book to read before you ever write your first code,” says Travis. “You'll learn about the nature of “code” from a historical perspective, what makes the code “code” (examples include Morse code, braille, and assembly if I recall) and it will explain some of the technical details about how code and hardware interact later on. One of my favorite books, and a pretty easy read.”

The Signal and the Noise: Why So Many Predictions Fail – But Some Don't by Nate Silver 

Recommended by: Aaron Gallant, Data Science Curriculum Lead

Summary: The book explores the world of prediction and forecasting, examining why some predictions succeed while others fail and what we can do to improve our predictions. Silver, who is best known for his work as a political analyst and statistician, draws on examples from a wide range of fields, including weather forecasting, sports, economics, and politics. He explores the common pitfalls of prediction, such as overconfidence, bias, and the tendency to ignore uncertainty.

It also provides insights into the tools and techniques used by successful forecasters, such as Bayesian statistics, simulation, and ensemble methods. Silver emphasizes the importance of combining multiple sources of information and using a rigorous, evidence-based approach to prediction.

Why you should read it: The Signal and the Noise is an eye-opening and thought-provoking book. It’s packed with fascinating stories and insights into the history of prediction, from the origins of weather forecasting to the rise of big data.

“It's not specifically about tech,” says Aaron. “But about predictions and statistics in society. A great way to get excited about the power of data.”

Practical Statistics for Data Scientists by Peter Bruce and Andrew Bruce

Recommended by: Aaron Gallant, Data Science Curriculum Lead

Summary: The book covers a wide range of statistical techniques, from simple descriptive statistics to more advanced methods such as regression analysis and machine learning. It is structured around practical examples and case studies, making it easy for readers to apply the concepts they have learned to real-world problems. The authors provide step-by-step instructions for using the R programming language to perform statistical analyses, as well as tips and tricks for interpreting and presenting the results.

After reading it, you will definitely understand the basic principles of statistical analysis. The authors explain the concepts and assumptions behind each method, as well as the strengths and limitations of different approaches. 

Why you should read it: Practical Statistics for Data Scientists is an essential resource for anyone working in the field of data science. It provides a solid foundation in statistical analysis, as well as practical tips and guidance for applying these techniques to real-world problems.

“It gives a solid overview of statistics for data scientists - definitely ‘mathy,’ but not too technical or advanced,” adds Aaron.

Python Data Science Handbook by Jake VanderPlas

Recommended by: Aaron Gallant, Data Science Curriculum Lead

Summary: Written by Jake VanderPlas, a data scientist and Python expert, the book provides a comprehensive introduction to the tools and techniques used in data science. The book covers a wide range of topics, including NumPy, Pandas, Matplotlib, Scikit-Learn, and more, providing readers with a solid foundation in the essential libraries and frameworks used in data science. VanderPlas provides step-by-step instructions for using Python to perform everyday data science tasks such as data cleaning, manipulation, and visualization. He also provides examples of more advanced techniques such as machine learning and natural language processing, making it a valuable resource for both beginners and experienced data scientists.

Why you should read it:  If you are interested in data science and want to learn how to use the Python programming language to perform data analysis and visualization, then the Python Data Science Handbook is an excellent resource.

“This one is just a great overall textbook, and it is freely available - so win-win!”

How to Learn to Code & Get a Developer Job in 2023 by Quincy Larson

Recommended by: Pavel Ivanovsky, Technical Editor 

Summary: “It may take you a few hours to read all this. But this is it. My insights into learning to code and getting a developer job,” says Quincy Larson. And that's the best description of the book: it's all about the experience of the author, who once worked as a teacher and didn't think about programming. In just one year he learned to program and got his first job. And a few years later he started, which is used by people all over the world. 

Why you should read it:  Because nothing inspires more than a personal example, and this book is beautifully written, perfectly structured, and completely free. 

“This book is perfect for those who are considering a getting-into-tech career change from the guy who did just that,” says Pavel. “The text provides answers to questions like ’how to network’, ’which programming language to learn?’ and ‘should I get a CS degree?’”

Structure and Interpretation of Computer Programs (SICP) by Harold Abelson and Gerald Jay Sussman with Julie Sussman

Recommended by: Ana Mineeva, Career Product Lead

Summary: Structure and Interpretation of Computer Programs (SICP) is a classic computer science textbook that was first published in 1985. The book teaches the principles of computer programming and computer science through the use of the Scheme programming language. Scheme is a dialect of Lisp, which is a functional programming language that emphasizes the use of recursion and abstraction. The book emphasizes the importance of understanding the fundamental concepts of computer science and programming rather than just learning how to write code in a particular language. 

Why you should read it: The book has had a profound influence on the field of computer science and has been used as a textbook in many computer science courses worldwide. It is highly regarded for its clear and concise writing style, its emphasis on fundamental concepts, and its practical applications of those concepts. It's a classic!

“It may seem complicated, but for me, it’s one of the best books,” Ana says. “It could help you see the big picture and increase your ability to find patterns.”


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