Books: 64


Summary Geoprocessing with Python teaches you how to use the Python programming language, along with free and open source tools, to read, write, and process geospatial data. About the Technology This book is about the science of reading, analyzing, and presenting geospatial data programmatically, more » using Python. Thanks to dozens of open source Python libraries and tools, you can take on professional geoprocessing tasks without investing in expensive proprietary packages like ArcGIS and MapInfo. The book shows you how. About the Book Geoprocessing with Python teaches you how to access available datasets to make maps or perform your own analyses using free tools like the GDAL, NumPy, and matplotlib Python modules. Through lots of hands-on examples, you’ll master core practices like handling multiple vector file formats, editing geometries, applying spatial and attribute filters, working with projections, and performing basic analyses on vector data. The book also covers how to manipulate, resample, and analyze raster data, such as aerial photographs and digital elevation models. What's Inside * Geoprocessing from the ground up * Read, write, process, and analyze raster data * Visualize data with matplotlib * Write custom geoprocessing tools * Three additional appendixes available online About the Reader To read this book all you need is a basic knowledge of Python or a similar programming language. About the Author Chris Garrard works as a developer for Utah State University and teaches a graduate course on Python programming for GIS.Table of Contents 1. Introduction 2. Python basics 3. Reading and writing vector data 4. Working with different vector file formats 5. Filtering data with OGR 6. Manipulating geometries with OGR 7. Vector analysis with OGR 8. Using spatial reference systems 9. Reading and writing raster data 10. Working with raster data 11. Map algebra with NumPy and SciPy 12. Map classification 13. Visualizing data 14. Appendixes * A - Installation * B - References * C - OGR - online only * D - OSR - online only * E - GDAL - online only « less
Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0
About This Book Perform data analysis and build predictive models on huge datasets that leverage Apache Spark Learn to integrate data science algorithms and techniques with the fast and scalable computing features of Spark to address big data challenges Work through practical examples on real-world more » problems with sample code snippets Who This Book Is For This book is for anyone who wants to leverage Apache Spark for data science and machine learning. If you are a technologist who wants to expand your knowledge to perform data science operations in Spark, or a data scientist who wants to understand how algorithms are implemented in Spark, or a newbie with minimal development experience who wants to learn about Big Data Analytics, this book is for you! What You Will Learn Consolidate, clean, and transform your data acquired from various data sources Perform statistical analysis of data to find hidden insights Explore graphical techniques to see what your data looks like Use machine learning techniques to build predictive models Build scalable data products and solutions Start programming using the RDD, DataFrame and Dataset APIs Become an expert by improving your data analytical skills In Detail This is the era of Big Data. The words ‘Big Data’implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages. Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R. « less
The Encyclopedia of Automotive Engineering provides for the first time a large, unified knowledge base laying the foundation for advanced study and in-depth research. Through extensive cross-referencing and search functionality it provides a gateway to detailed but scattered information on best industry more » practice, engendering a better understanding of interrelated concepts and techniques that cut across specialized areas of engineering. Beyond traditional automotive subjects the Encyclopedia addresses green technologies, the shift from mechanics to electronics, and the means to produce safer, more efficient vehicles within varying economic restraints worldwide. « less
This book provides guidance on including prevention through design concepts within an occupational safety and health management system. Through the application of these concepts, decisions pertaining to occupational hazards and risks can be incorporated into the process of design and redesign of work more » premises, tools, equipment, machinery, substances, and work processes including their construction, manufacture, use, maintenance, and ultimate disposal or reuse. These techniques provide guidance for a life-cycle assessment and design model that balances environmental and occupational safety and health goals over the life span of a facility, process, or product. « less
How Analytics and BI are Transforming the Industry
Data is taking over in a powerful way, and it's revolutionizing the healthcare industry. You have more data available than ever before, and applying the right analytics can spur growth. Benefits extend to patients, providers, and board members, and the technology can make centralized patient management more » a reality. Despite the potential for growth, many in the industry and government are questioning the value of data in health care, wondering if it's worth the investment. Data-Driven Healthcare: How Analytics and BI are Transforming the Industry tackles the issue and proves why BI is not only worth it, but necessary for industry advancement. Healthcare BI guru Laura Madsen challenges the notion that data have little value in healthcare, and shows how BI can ease regulatory reporting pressures and streamline the entire system as it evolves. Madsen illustrates how a data-driven organization is created, and how it can transform the industry. « less
A Practical Experience
Engineering Innovative Products: A Practical Experience is a pioneering book that will be of key use to senior undergraduate and graduate engineering students who are being encouraged to explore innovation and commercialization as part of their courses. The book will teach the essential skills of entrepreneurship more » and address the fundamental requirements needed to establish a successful technology company. As well as providing the crucial background and insights enabling students to identify a key market, it also offers a highly practical guide to undertaking genuine product validation and producing a feasibility study, as well as providing vital insights into the challenges and demands in forming a technology based company. « less
Hands-On for Developers and Technical Professionals
Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works more » and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. « less
Apply effective learning algorithms to real-world problems using scikit-learn
This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised more » spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features. You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models. The book will also walk you through an example project that prompts you to label the most uncertain training examples. You will also use an unsupervised Hidden Markov Model to predict stock prices. « less
89 hands-on recipes to help you complete real-world data science projects in R and Python
Starting with the basics, this book will cover how to set up your numerical programming environment, introduce you to the data science pipeline (an iterative process by which data science projects are completed), and guide you through several data projects in a step-by-step format. By sequentially working more » through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples in the two most popular programming languages for data analysis - R and Python. « less
Schaum's has Satisfied Students for 50 Years. Now Schaum's Biggest Sellers are in New Editions! For half a century, more than 40 million students have trusted Schaum's to help them study faster, learn better, and get top grades. Now Schaum's celebrates its 50th birthday with a brand-new look, a more » new format with hundreds of practice problems, and completely updated information to conform to the latest developments in every field of study. Schaum's Outlines-Problem Solved Linear algebra is a foundation course for students entering mathematics, engineering, and computer science, and the fourth edition includes more problems connected directly with applications to these majors. It is also updated throughout to include new essential appendices in algebraic systems, polynomials, and matrix applications. « less