Focusing on parallel data structures and algorithms, this book serves as a comprehensive guide for those interested in parallel computing within data science. It equips readers with the skills to write effective parallel code across multiple programming languages and explores various R packages and tools. The content includes discussions on the classic "n observations, p variables" matrix format, alongside common data structures, complemented by numerous examples that highlight the challenges faced in parallel programming.
Learn to expertly apply a range of machine learning methods to real data with this practical guide.Machine learning without advanced math! This book presents a serious, practical look at machine learning, preparing you for valuable insights on your own data. The Art of Machine Learning is packed with real dataset examples and sophisticated advice on how to make full use of powerful machine learning methods. Readers will need only an intuitive grasp of charts, graphs, and the slope of a line, as well as familiarity with the R programming language.You’ll become skilled in a range of machine learning methods, starting with the simple k-Nearest Neighbors method (k-NN), then on to random forests, gradient boosting, linear/logistic models, support vector machines, the LASSO, and neural networks. Final chapters introduce text and image classification, as well as time series. You’ll learn not only how to use machine learning methods, but also why these methods work, providing the strong foundational background you’ll need in practice.Additional After finishing this book, you will be well equipped to start applying machine learning techniques to your own datasets.
Targeted at data science students, this textbook covers probability and statistics through a calculus-based approach suitable for junior, senior, and graduate levels. It integrates foundational concepts from matrix algebra and basic programming skills, making it ideal for those in computer science. The content is structured to facilitate understanding of statistical methods in data analysis, catering to the needs of students preparing for advanced applications in their fields.
Focusing on regression and classification, this text offers a contemporary approach tailored for big data analysis using R. It minimizes mathematical statistics, presenting concepts in practical contexts, allowing readers to bypass the math if preferred. This accessibility makes it suitable for a wide audience interested in data science without heavy mathematical prerequisites.
R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly. The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro. Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: –Create artful graphs to visualize complex data sets and functions –Write more efficient code using parallel R and vectorization –Interface R with C/C++ and Python for increased speed or functionality –Find new R packages for text analysis, image manipulation, and more –Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.
