{"title":"Data Science \u0026 Statistics","description":"\u003cp\u003eStatistical learning, deep learning and research methods: the standard references for quantitative coursework and self-study.\u003c\/p\u003e","products":[{"product_id":"deep-learning-adaptive-computation-and-machine-learning-series-isbn-9780262035613","title":"Deep Learning (Adaptive Computation and Machine Learning series)","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003eAn introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.\u003c\/p\u003e\n \u003cp\u003e“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”\u003cbr\u003e—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX\u003c\/p\u003e\n \u003cp\u003eDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.\u003c\/p\u003e\n \u003cp\u003eThe text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.\u003c\/p\u003e\n \u003cp\u003eDeep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.\u003c\/p\u003e","brand":"US BookGram","offers":[{"title":"Default Title","offer_id":53348004692335,"sku":"BOOK-9780262035613","price":49.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0989\/8573\/8607\/files\/9780262035613.jpg?v=1787424723"},{"product_id":"statistical-methods-for-the-social-sciences-5th-edition-isbn-9780134507101","title":"Statistical Methods for the Social Sciences 5th Edition","description":"\u003cp class=\"a-text-italic\"\u003eFor courses in Statistical Methods for the Social Sciences .\u003c\/p\u003e\n \u003cp\u003eStatistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes you have no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The 5th Edition gives you examples and exercises with a variety of “real data.” It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics–often a stumbling block for students–while avoiding reliance on an overly simplistic recipe-based approach to statistics.\u003c\/p\u003e","brand":"US BookGram","offers":[{"title":"Default Title","offer_id":53348004888943,"sku":"BOOK-9780134507101","price":74.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0989\/8573\/8607\/files\/9780134507101.jpg?v=1787424723"},{"product_id":"the-elements-of-statistical-learning-data-mining-inference-and-prediction-second-edition-springer-series-in-statistics-2nd-edition-isbn-9780387848570","title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) 2nd Edition","description":"\u003cp\u003eThis book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.\u003c\/p\u003e\n \u003cp\u003eThis major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression \u0026amp; path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for \"wide'' data (p bigger than n), including multiple testing and false discovery rates.\u003c\/p\u003e","brand":"US BookGram","offers":[{"title":"Default Title","offer_id":53348004954479,"sku":"BOOK-9780387848570","price":54.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0989\/8573\/8607\/files\/9780387848570.jpg?v=1787729448"},{"product_id":"an-introduction-to-statistical-learning-with-applications-in-r-springer-texts-in-statistics-2nd-ed-isbn-9781071614174","title":"An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) 2nd ed","description":"\u003cp\u003eAn Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.\u003c\/p\u003e\n \u003cp\u003eTwo of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.\u003c\/p\u003e\n \u003cp\u003e \u003c\/p\u003e\n \u003cp\u003eThis Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.\u003c\/p\u003e\n \u003cp\u003e \u003c\/p\u003e\n \u003cp\u003eFour of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.\u003c\/p\u003e","brand":"US BookGram","offers":[{"title":"Default Title","offer_id":53348005020015,"sku":"BOOK-9781071614174","price":54.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0989\/8573\/8607\/files\/9781071614174.jpg?v=1787424723"},{"product_id":"the-pragmatic-programmer-your-journey-to-mastery-20th-anniversary-edition-2nd-edition-isbn-9780135957059","title":"The Pragmatic Programmer: Your Journey To Mastery, 20th Anniversary Edition (2nd Edition)","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003e“One of the most significant books in my life.”—Obie Fernandez, Author,The Rails Way\u003cbr\u003e\u003cbr\u003e“Twenty years ago, the first edition of The Pragmatic Programmer completely changed the trajectory of my career. This new edition could do the same for yours.”—Mike Cohn, Author ofSucceeding with Agile,Agile Estimating and Planning, andUser Stories Applied\u003cbr\u003e\u003cbr\u003e“. filled with practical advice, both technical and professional, that will serve you and your projects well for years to come.”—Andrea Goulet, CEO, Corgibytes, Founder, LegacyCode.Rocks\u003cbr\u003e\u003cbr\u003e“. . . lightning does strike twice, and this book is proof.”—VM (Vicky) Brasseur, Director of Open Source Strategy, Juniper Networks\u003cbr\u003eThe Pragmatic Programmer is one of those rare tech books you’ll read, re-read, and read again over the years. Whether you’re new to the field or an experienced practitioner, you’ll come away with fresh insights each and every time.\u003cbr\u003e\u003c\/p\u003e\n \u003cp\u003e\u003cmeta charset=\"utf-8\"\u003eDave Thomas and Andy Hunt wrote the first edition of this influential book in 1999 to help their clients create better software and rediscover the joy of coding. These lessons have helped a generation of programmers examine the very essence of software development, independent of any particular language, framework, or methodology, and the Pragmatic philosophy has spawned hundreds of books, screencasts, and audio books, as well as thousands of careers and success stories.\u003cbr\u003e\u003cbr\u003eNow, twenty years later, this new edition re-examines what it means to be a modern programmer. Topics range from personal responsibility and career development to architectural techniques for keeping your code flexible and easy to adapt and reuse. Read this book, and you’ll learn how to:\u003cbr\u003e\u003c\/p\u003e\n \u003cul class=\"a-unordered-list a-vertical\"\u003e\n \u003cli\u003eFight software rot\u003c\/li\u003e\n \u003cli\u003eLearn continuously\u003c\/li\u003e\n \u003cli\u003eAvoid the trap of duplicating knowledge\u003c\/li\u003e\n \u003cli\u003eWrite flexible, dynamic, and adaptable code\u003c\/li\u003e\n \u003cli\u003eHarness the power of basic tools\u003c\/li\u003e\n \u003cli\u003eAvoid programming by coincidence\u003c\/li\u003e\n \u003cli\u003eLearn real requirements\u003c\/li\u003e\n \u003cli\u003eSolve the underlying problems of concurrent code\u003c\/li\u003e\n \u003cli\u003eGuard against security vulnerabilities\u003c\/li\u003e\n \u003cli\u003eBuild teams of Pragmatic Programmers\u003c\/li\u003e\n \u003cli\u003eTake responsibility for your work and career\u003c\/li\u003e\n \u003cli\u003eTest ruthlessly and effectively, including property-based testing\u003c\/li\u003e\n \u003cli\u003eImplement the Pragmatic Starter Kit\u003c\/li\u003e\n \u003cli\u003eDelight your users\u003c\/li\u003e\n \u003c\/ul\u003e\n \u003cp\u003eWritten as a series of self-contained sections and filled with classic and fresh anecdotes, thoughtful examples, and interesting analogies, The Pragmatic Programmer illustrates the best approaches and major pitfalls of many different aspects of software development. Whether you’re a new coder, an experienced programmer, or a manager responsible for software projects, use these lessons daily, and you’ll quickly see improvements in personal productivity, accuracy, and job satisfaction. You’ll learn skills and develop habits and attitudes that form the foundation for long-term success in your career.\u003cbr\u003e\u003cbr\u003eYou’ll become a Pragmatic Programmer.\u003cbr\u003e\u003c\/p\u003e","brand":"US BookGram","offers":[{"title":"Default Title","offer_id":53348005282159,"sku":"BOOK-9780135957059","price":49.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0989\/8573\/8607\/files\/9780135957059.jpg?v=1787424723"}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0989\/8573\/8607\/collections\/data-science.png?v=1787380397","url":"https:\/\/usbookgram.com\/collections\/data-science-statistics.oembed","provider":"US BookGram","version":"1.0","type":"link"}