Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling statistical questions.
Contemporary data science requires a tight integration of knowledge from statistics, computer science, mathematics, and a domain of application. This book will help readers with some background in statistics and modest prior experience with coding develop and practice the appropriate skills to tackle complex data science projects. The book features a number of exercises and has a flexible organization conducive to teaching a variety of semester courses.
Lean Python: Learn Just Enough Python to Build Useful Tools
Learn only the essential aspects of Python without cluttering up your mind with features you may never use. This compact book is not a "best way to write code" type of book; rather, the author goes over his most-used functions, which are all you need to know as a beginner and some way beyond.
Begin to Code with Python
Become a Python programmer–and have fun doing it!
Start writing software that solves real problems, even if you have absolutely no programming experience! This friendly, easy, full-color book puts you in total control of your own learning, empowering you to build...
Hacking the Hacker: Learn From the Experts Who Take Down Hackers
Meet the world's top ethical hackers and explore the tools of the trade
Hacking the Hacker takes you inside the world of cybersecurity to show you what goes on behind the scenes, and introduces you to the men and women on the front lines of this technological arms race. Twenty-six of the world's top white hat...
Probability and Statistics for Computer Scientists
Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools
Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic...