Addison-wesley Data & Analytics - 14 Book Series (447 results)

Paperback. Condition: Good. Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution.   Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you?ll need to accomplish 80 percent of modern data tasks. Lander?s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You?ll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you?ll construct several complete models, both linear and nonlinear, and use some data mining techniques.   By the time you?re done, you won?t just know how to write R programs, you?ll be ready to tackle the statistical problems you care about most.   Coverage Includes: Exploring R, RStudio, and R packages Using R for math: variable types, vectors, calling functions, and more Exploiting data structures, including data.frames, matrices, and lists Creating attractive, intuitive statistical graphics Writing user-defined functions Controlling program flow with if, ifelse, and complex checks Improving program efficiency with group manipulations Combining and reshaping multiple datasets Manipulating strings using R?s facilities and regular expressions Creating normal, binomial, and Poisson probability distributions Programming basic statistics: mean, standard deviation, and t-tests Building linear, generalized linear, and nonlinear models Assessing the quality of models and variable selection Preventing overfitting, using the Elastic Net and Bayesian methods Analyzing univariate and multivariate time series data Grouping data via K-means and hierarchical clustering Preparing reports, slideshows, and web pages with knitr Building reusable R packages with devtools and Rcpp Getting involved with the R global community.…

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Paperback. Condition: Good. Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution.   Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you?ll need to accomplish 80 percent of modern data tasks. Lander?s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You?ll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you?ll construct several complete models, both linear and nonlinear, and use some data mining techniques.   By the time you?re done, you won?t just know how to write R programs, you?ll be ready to tackle the statistical problems you care about most.   Coverage Includes: Exploring R, RStudio, and R packages Using R for math: variable types, vectors, calling functions, and more Exploiting data structures, including data.frames, matrices, and lists Creating attractive, intuitive statistical graphics Writing user-defined functions Controlling program flow with if, ifelse, and complex checks Improving program efficiency with group manipulations Combining and reshaping multiple datasets Manipulating strings using R?s facilities and regular expressions Creating normal, binomial, and Poisson probability distributions Programming basic statistics: mean, standard deviation, and t-tests Building linear, generalized linear, and nonlinear models Assessing the quality of models and variable selection Preventing overfitting, using the Elastic Net and Bayesian methods Analyzing univariate and multivariate time series data Grouping data via K-means and hierarchical clustering Preparing reports, slideshows, and web pages with knitr Building reusable R packages with devtools and Rcpp Getting involved with the R global community.…

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Paperback. Condition: Good. This is the first end-to-end, full-color guide to telling powerful, actionable data stories using Tableau, the world?s #1 visualization software. Renowned expert Lindy Ryan shows you how to communicate the full business implications of your data analyses by combining Tableau?s remarkable capabilities with a deep understanding of storytelling and design.   Each chapter illuminates key aspects of design practice and data visualization, and guides you step-by-step through applying them in Tableau. Ryan demonstrates how ?data stories? resemble and differ from traditional storytelling, and helps you use Tableau to analyze, visualize, and communicate insights that are meaningful to any stakeholder, in any medium.   Information Visualization in Tableau presents exercises that give you hands-on practice with the most up-to-date capabilities available through Tableau 10 and the full Tableau software ecosystem. Ryan?s classroom-tested exercises won?t just help you master the software: they?ll show you to craft data stories that inspire action.   Coverage includes: The visual data storytelling paradigm: moving beyond static charts to powerful visualizations that combine narrative with interactive graphics How to think like a data scientist, a storyteller, and a designer -- all in the same project Data storytelling case studies: the good, the bad, and the ugly Shaping data stories: blending data science, genre, and visual design Seven best practices for visual data storytelling -- and common pitfalls to avoid Tricks and hacks you can use with any toolset, not just Tableau.…

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Paperback. Condition: Good. This is the first end-to-end, full-color guide to telling powerful, actionable data stories using Tableau, the world?s #1 visualization software. Renowned expert Lindy Ryan shows you how to communicate the full business implications of your data analyses by combining Tableau?s remarkable capabilities with a deep understanding of storytelling and design.   Each chapter illuminates key aspects of design practice and data visualization, and guides you step-by-step through applying them in Tableau. Ryan demonstrates how ?data stories? resemble and differ from traditional storytelling, and helps you use Tableau to analyze, visualize, and communicate insights that are meaningful to any stakeholder, in any medium.   Information Visualization in Tableau presents exercises that give you hands-on practice with the most up-to-date capabilities available through Tableau 10 and the full Tableau software ecosystem. Ryan?s classroom-tested exercises won?t just help you master the software: they?ll show you to craft data stories that inspire action.   Coverage includes: The visual data storytelling paradigm: moving beyond static charts to powerful visualizations that combine narrative with interactive graphics How to think like a data scientist, a storyteller, and a designer -- all in the same project Data storytelling case studies: the good, the bad, and the ugly Shaping data stories: blending data science, genre, and visual design Seven best practices for visual data storytelling -- and common pitfalls to avoid Tricks and hacks you can use with any toolset, not just Tableau.…

Paperback. Condition: Very Good. The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like ChatGPT are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, hands-on exercises, and more. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, parameters, and performance. You'll find even more resources on the companion website, including sample datasets and code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and ChatGPT), Google (BERT, T5, and Bard), EleutherAI (GPT-J and GPT-Neo), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and moreUse APIs and Python to fine-tune and customize LLMs for your requirementsBuild a complete neural/semantic information retrieval system and attach to conversational LLMs for retrieval-augmented generationMaster advanced prompt engineering techniques like output structuring, chain-ofthought, and semantic few-shot promptingCustomize LLM embeddings to build a complete recommendation engine from scratch with user dataConstruct and fine-tune multimodal Transformer architectures using opensource LLMsAlign LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF)Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind "By balancing the potential of both open- and closed-source models, Quick Start Guide to Large Language Models stands as a comprehensive guide to understanding and using LLMs, bridging the gap between theoretical concepts and practical application."--Giada Pistilli, Principal Ethicist at HuggingFace "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field."--Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.…