Found insideDrawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace provide practical advice on a range of topics—from optimizing the set-up of RStudio to leveraging C++—that make this book a useful addition to ... Found inside – Page 1This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. Found inside – Page iiiWritten for statisticians, computer scientists, geographers, research and applied scientists, and others interested in visualizing data, this book presents a unique foundation for producing almost every quantitative graphic found in ... Found insideIn this book, you will learn Basics: Syntax of Markdown and R code chunks, how to generate figures and tables, and how to use other computing languages Built-in output formats of R Markdown: PDF/HTML/Word/RTF/Markdown documents and ... This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data ... Found inside – Page 1About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Found insideThis practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham’s package development philosophy. Welcome It's a book to learn data science, machine learning and data analysis with tons of examples and explanations around several topics like: Exploratory data analysis Data preparation Selecting best variables Model performance Note: ... This book serves as a basic guide for a wide range of audiences from less familiar with metabolomics techniques to more experienced researchers seeking to understand complex biological systems from the systems biology approach. Found inside – Page 10A professional data scientist should hedge his career by not depending on just one ... r‐vs‐python‐data‐science.html by a founder of DataCamp and at http:// ... While the text is biased against complex equations, a mathematical background is needed for advanced topics. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Found insideWith this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas ... Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. "This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"-- This book will get you there. About the Book Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Found inside – Page 1But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? Found insideBecome an efficient data scientist with R About This Book Explore the R language from basic types and data structures to advanced topics Learn how to tackle programming problems and explore both functional and object-oriented programming ... Getting started with testing can be hard, and this book aims make it all very easy by using examples and explaining the process in a straightforward way. Found insideThe book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Presents case studies and instructions on how to solve data analysis problems using Python. With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. Chapter 7. Found insideThis volume offers an overview of current efforts to deal with dataset and covariate shift. Found insideIn this book, you'll learn to implement some practical and proven techniques to improve aspects of programming and administration in Apache Spark. Found inside – Page iThe Python Workbook provides a compendium of 186 exercises, spanning a variety of academic disciplines and everyday situations. Found insideAbout the Book R in Action, Second Edition teaches you how to use the R language by presenting examples relevant to scientific, technical, and business developers. This book is intended for Python programmers, mathematicians, and analysts who already have a basic understanding of Python and wish to learn about its data analysis capabilities in depth. In this guide, Big Data expert Jeffrey Aven covers all students need to know to leverage Spark, together with its extensions, subprojects, and wider ecosystem. Found insideThis book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science. Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists. Found insideIn this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. This is where this book helps. The data science solutions book provides a repeatable, robust, and reliable framework to apply the right-fit workflows, strategies, tools, APIs, and domain for your data science projects. Found inside – Page iWho This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Found insideUtilize R to uncover hidden patterns in your Big Data About This Book Perform computational analyses on Big Data to generate meaningful results Get a practical knowledge of R programming language while working on Big Data platforms like ... Found insideUnleash the power and flexibility of the Bayesian framework About This Book Simplify the Bayes process for solving complex statistical problems using Python; Tutorial guide that will take the you through the journey of Bayesian analysis ... 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