Practical Statistics for Data Scientists, 2nd Edition
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you'll learn: Why exploratory data analysis is a key preliminary step in data science; How random sampling can reduce bias and yield a higher-quality dataset, even with big data; How the principles of experimental design yield definitive answers to ...
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you'll learn: Why exploratory data analysis is a key preliminary step in data science; How random sampling can reduce bias and yield a higher-quality dataset, even with big data; How the principles of experimental design yield definitive answers to ...
High Performance Python, 2nd Edition
Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python's implementation. How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more. Get a better grasp of NumPy, Cython, and profilers; Learn how Python abstracts the underlying computer architecture; Use profiling to find bottlenecks in CPU time and memory usage; Write efficient programs by choosing appropriate data structures; Speed up matrix ...
Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python's implementation. How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more. Get a better grasp of NumPy, Cython, and profilers; Learn how Python abstracts the underlying computer architecture; Use profiling to find bottlenecks in CPU time and memory usage; Write efficient programs by choosing appropriate data structures; Speed up matrix ...
Learning Java, 5th Edition
If you're new to Java - or new to programming - this best-selling book will guide you through the language features and APIs of Java 11. With fun, compelling, and realistic examples, authors Marc Loy, Patrick Niemeyer, and Daniel Leuck introduce you to Java fundamentals - including its class libraries, programming techniques, and idioms - with an eye toward building real applications. You'll learn powerful new ways to manage resources and exceptions in your applications - along with core language features included in recent Java versions. Develop with Java, using the compiler, interpreter, and other tools; Explore Java's built-in thread facilities and concurrency package; Learn text processing and the powerful regular expressions API; Write advanced networked or web-based applications and services. ...
If you're new to Java - or new to programming - this best-selling book will guide you through the language features and APIs of Java 11. With fun, compelling, and realistic examples, authors Marc Loy, Patrick Niemeyer, and Daniel Leuck introduce you to Java fundamentals - including its class libraries, programming techniques, and idioms - with an eye toward building real applications. You'll learn powerful new ways to manage resources and exceptions in your applications - along with core language features included in recent Java versions. Develop with Java, using the compiler, interpreter, and other tools; Explore Java's built-in thread facilities and concurrency package; Learn text processing and the powerful regular expressions API; Write advanced networked or web-based applications and services. ...
Practical Synthetic Data Generation
Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data - fake data generated from real data - so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue. Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. This book describes: Steps for generating synthetic data using multivariate normal distributions; Methods for distribution fitting covering different goodness-of-fit metrics; How to replicate the simple structure of orig ...
Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data - fake data generated from real data - so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue. Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. This book describes: Steps for generating synthetic data using multivariate normal distributions; Methods for distribution fitting covering different goodness-of-fit metrics; How to replicate the simple structure of orig ...
Graph Algorithms
Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. With this practical guide, developers and data scientists will discover how graph analytics deliver value, whether they're used for building dynamic network models or forecasting real-world behavior. Mark Needham and Amy Hodler from Neo4j explain how graph algorithms describe complex structures and reveal difficult-to-find patterns - from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. You'll walk through hands-on examples that show you how to use graph algorithms in Apache Spark and Neo4j, two of the most common choices for graph analytics. Learn how graph analytics reveal more predictive elements in today's data; Understand how popular graph algorithms work and how they're applied; Use sample code and tips from more than 20 graph algorithm examples; Learn which alg ...
Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. With this practical guide, developers and data scientists will discover how graph analytics deliver value, whether they're used for building dynamic network models or forecasting real-world behavior. Mark Needham and Amy Hodler from Neo4j explain how graph algorithms describe complex structures and reveal difficult-to-find patterns - from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. You'll walk through hands-on examples that show you how to use graph algorithms in Apache Spark and Neo4j, two of the most common choices for graph analytics. Learn how graph analytics reveal more predictive elements in today's data; Understand how popular graph algorithms work and how they're applied; Use sample code and tips from more than 20 graph algorithm examples; Learn which alg ...
The Project Managers Guide to Microsoft Project 2019
This guide is an all-in-one training resource and reference that covers all versions found in the Microsoft Project 2019 suite. It is not a "how-to" manual covering the features and functions of the software, but is designed to explain and demonstrate why those features and functions are important to you as a project manager, allowing you to maximize the value of Microsoft Project 2019. Each aspect of project-manager-specific coverage was selectively compiled by author and Microsoft Project expert Cicala over more than two decades of consulting, project management training, and managing real-world projects using Microsoft Project. Readers will appreciate the robust index and intuitively organized and learning-oriented chapters, and sub-sections for quick reference and problem solving. "Try it" exercises at the close of every chapter help ensure understanding of the content. ...
This guide is an all-in-one training resource and reference that covers all versions found in the Microsoft Project 2019 suite. It is not a "how-to" manual covering the features and functions of the software, but is designed to explain and demonstrate why those features and functions are important to you as a project manager, allowing you to maximize the value of Microsoft Project 2019. Each aspect of project-manager-specific coverage was selectively compiled by author and Microsoft Project expert Cicala over more than two decades of consulting, project management training, and managing real-world projects using Microsoft Project. Readers will appreciate the robust index and intuitively organized and learning-oriented chapters, and sub-sections for quick reference and problem solving. "Try it" exercises at the close of every chapter help ensure understanding of the content. ...
TensorFlow Roadmap
A deep learning is of great interest these days, the crucial necessity for rapid and optimized implementation of the algorithms and designing architectures is the software environment. TensorFlow is designed to facilitate this goal. The strong advantage of TensorFlow is it flexibility is designing highly modular model which also can be a disadvantage too for beginners since lots of the pieces must be considered together for creating the model. This issue has been facilitated as well by developing high-level APIs such as Keras and Slim which gather lots of the design puzzle pieces. The interesting point about TensorFlow is that its trace can be found anywhere these days. Lots of the researchers and developers are using it and its community is growing with the speed of light! So the possible issues can be overcame easily since they might be the issues of lots of other people considering a large number of people involved in TensorFlow community. ...
A deep learning is of great interest these days, the crucial necessity for rapid and optimized implementation of the algorithms and designing architectures is the software environment. TensorFlow is designed to facilitate this goal. The strong advantage of TensorFlow is it flexibility is designing highly modular model which also can be a disadvantage too for beginners since lots of the pieces must be considered together for creating the model. This issue has been facilitated as well by developing high-level APIs such as Keras and Slim which gather lots of the design puzzle pieces. The interesting point about TensorFlow is that its trace can be found anywhere these days. Lots of the researchers and developers are using it and its community is growing with the speed of light! So the possible issues can be overcame easily since they might be the issues of lots of other people considering a large number of people involved in TensorFlow community. ...
Python One-Liners
Python One-Liners will teach you how to read and write "one-liners": concise statements of useful functionality packed into a single line of code. You'll learn how to systematically unpack and understand any line of Python code, and write eloquent, powerfully compressed Python like an expert. The book's five chapters cover tips and tricks, regular expressions, machine learning, core data science topics, and useful algorithms. Detailed explanations of one-liners introduce key computer science concepts and boost your coding and analytical skills. You'll learn about advanced Python features such as list comprehension, slicing, lambda functions, regular expressions, map and reduce functions, and slice assignments. You'll also learn how to: Leverage data structures to solve real-world problems, like using Boolean indexing to find cities with above-average pollution; Use NumPy basics such as array, shape, axis, type, broadcasting, advanced indexing, slicing, sorting, searching, aggr ...
Python One-Liners will teach you how to read and write "one-liners": concise statements of useful functionality packed into a single line of code. You'll learn how to systematically unpack and understand any line of Python code, and write eloquent, powerfully compressed Python like an expert. The book's five chapters cover tips and tricks, regular expressions, machine learning, core data science topics, and useful algorithms. Detailed explanations of one-liners introduce key computer science concepts and boost your coding and analytical skills. You'll learn about advanced Python features such as list comprehension, slicing, lambda functions, regular expressions, map and reduce functions, and slice assignments. You'll also learn how to: Leverage data structures to solve real-world problems, like using Boolean indexing to find cities with above-average pollution; Use NumPy basics such as array, shape, axis, type, broadcasting, advanced indexing, slicing, sorting, searching, aggr ...
React and React Native, 3rd Edition
React and React Native, Facebook's innovative User Interface (UI) libraries, are designed to help you build robust cross-platform web and mobile applications. This updated third edition is improved and updated to cover the latest version of React. The book particularly focuses on the latest developments in the React ecosystem, such as modern Hook implementations, code splitting using lazy components and Suspense, user interface framework components using Material-UI, and Apollo. In terms of React Native, the book has been updated to version 0.62 and demonstrates how to apply native UI components for your existing mobile apps using NativeBase. You will begin by learning about the essential building blocks of React components. Next, you'll progress to working with higher-level functionalities in application development, before putting this knowledge to use by developing user interface components for the web and for native platforms. In the concluding chapters, you'll learn how to brin ...
React and React Native, Facebook's innovative User Interface (UI) libraries, are designed to help you build robust cross-platform web and mobile applications. This updated third edition is improved and updated to cover the latest version of React. The book particularly focuses on the latest developments in the React ecosystem, such as modern Hook implementations, code splitting using lazy components and Suspense, user interface framework components using Material-UI, and Apollo. In terms of React Native, the book has been updated to version 0.62 and demonstrates how to apply native UI components for your existing mobile apps using NativeBase. You will begin by learning about the essential building blocks of React components. Next, you'll progress to working with higher-level functionalities in application development, before putting this knowledge to use by developing user interface components for the web and for native platforms. In the concluding chapters, you'll learn how to brin ...
Mastering Adobe Photoshop Elements 2020, 2nd Edition
Adobe Photoshop Elements is a raster graphics editor for entry-level photographers, image editors, and hobbyists. Updated and improved to cover the latest features of Photoshop Elements 2020, this second edition includes focused coverage of Adobe's new AI-powered features that are designed to make the editing process more efficient, creative and fun. This book takes you through the complexities of image editing in easy-to-follow, bite-sized chunks, helping you to quickly recognize the editing challenge at hand and use suitable tools and techniques to overcome it. You'll start by learning how to import, organize, manage, edit, and use your pictures in a format that's designed for creative photography projects. Throughout this Adobe Photoshop Elements book, you'll discover how to fix different photographic problems using an extensive repertoire of commonly applied solutions. Common processes such as applying artistic effects to creative projects, custom image makeovers, processing ima ...
Adobe Photoshop Elements is a raster graphics editor for entry-level photographers, image editors, and hobbyists. Updated and improved to cover the latest features of Photoshop Elements 2020, this second edition includes focused coverage of Adobe's new AI-powered features that are designed to make the editing process more efficient, creative and fun. This book takes you through the complexities of image editing in easy-to-follow, bite-sized chunks, helping you to quickly recognize the editing challenge at hand and use suitable tools and techniques to overcome it. You'll start by learning how to import, organize, manage, edit, and use your pictures in a format that's designed for creative photography projects. Throughout this Adobe Photoshop Elements book, you'll discover how to fix different photographic problems using an extensive repertoire of commonly applied solutions. Common processes such as applying artistic effects to creative projects, custom image makeovers, processing ima ...