Python Data Science Handbook


Book Description

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With 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: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms




The Fingerprint


Book Description

The idea of The Fingerprint Sourcebook originated during a meeting in April 2002. Individuals representing the fingerprint, academic, and scientific communities met in Chicago, Illinois, for a day and a half to discuss the state of fingerprint identification with a view toward the challenges raised by Daubert issues. The meeting was a joint project between the International Association for Identification (IAI) and West Virginia University (WVU). One recommendation that came out of that meeting was a suggestion to create a sourcebook for friction ridge examiners, that is, a single source of researched information regarding the subject. This sourcebook would provide educational, training, and research information for the international scientific community.




The Emperor of All Maladies


Book Description

Winner of the Pulitzer Prize and a documentary from Ken Burns on PBS, this New York Times bestseller is “an extraordinary achievement” (The New Yorker)—a magnificent, profoundly humane “biography” of cancer—from its first documented appearances thousands of years ago through the epic battles in the twentieth century to cure, control, and conquer it to a radical new understanding of its essence. Physician, researcher, and award-winning science writer, Siddhartha Mukherjee examines cancer with a cellular biologist’s precision, a historian’s perspective, and a biographer’s passion. The result is an astonishingly lucid and eloquent chronicle of a disease humans have lived with—and perished from—for more than five thousand years. The story of cancer is a story of human ingenuity, resilience, and perseverance, but also of hubris, paternalism, and misperception. Mukherjee recounts centuries of discoveries, setbacks, victories, and deaths, told through the eyes of his predecessors and peers, training their wits against an infinitely resourceful adversary that, just three decades ago, was thought to be easily vanquished in an all-out “war against cancer.” The book reads like a literary thriller with cancer as the protagonist. Riveting, urgent, and surprising, The Emperor of All Maladies provides a fascinating glimpse into the future of cancer treatments. It is an illuminating book that provides hope and clarity to those seeking to demystify cancer.




Feature Engineering for Machine Learning


Book Description

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques




Mathematica Cookbook


Book Description

Mathematica Cookbook helps you master the application's core principles by walking you through real-world problems. Ideal for browsing, this book includes recipes for working with numerics, data structures, algebraic equations, calculus, and statistics. You'll also venture into exotic territory with recipes for data visualization using 2D and 3D graphic tools, image processing, and music. Although Mathematica 7 is a highly advanced computational platform, the recipes in this book make it accessible to everyone -- whether you're working on high school algebra, simple graphs, PhD-level computation, financial analysis, or advanced engineering models. Learn how to use Mathematica at a higher level with functional programming and pattern matching Delve into the rich library of functions for string and structured text manipulation Learn how to apply the tools to physics and engineering problems Draw on Mathematica's access to physics, chemistry, and biology data Get techniques for solving equations in computational finance Learn how to use Mathematica for sophisticated image processing Process music and audio as musical notes, analog waveforms, or digital sound samples




Art and Visual Perception


Book Description




GIS Cartography


Book Description

In the five years since the publication of the first edition of A Guide to Effective Map Design, cartography and software have become further intertwined. However, the initial motivation for publishing the first edition is still valid: many GISers enter the field without so much as one hour of design instruction in their formal education. Yet they are then tasked with creating one the most effective, easily recognized communication tools: a map. See What’s New in the Second Edition Projection theory Hexagonal binning Big Data point density maps Scale dependent map design 3D building modeling Digital cartography and its best practices Updated graphics and references Study questions and lab exercises at the end of each chapter In this second edition of a bestseller, author Gretchen Peterson takes a "don’t let the technology get in the way" approach to the presentation, focusing on the elements of good design, what makes a good map, and how to get there, rather than specific software tools. She provides a reference that you can thumb through time and again as you create your maps. Copiously illustrated, the second edition explores novel concepts that kick-start your pursuit of map-making excellence. The book doesn’t just teach you how to design and create maps, it teaches you how to design and create better maps.




Designing Science Presentations


Book Description

Designing Science Presentations: A Visual Guide to Figures, Papers, Slides, Posters, and More, Second Edition, guides scientists of any discipline in the design of compelling science communication. Most scientists never receive formal training in the design, delivery and evaluation of scientific communication, yet these skills are essential for publishing in high-quality journals, soliciting funding, attracting lab personnel, and advancing a career. This clear, readable volume fills that gap, providing visually intensive guidance at every step—from the construction of original figures to the presentation and delivery of those figures in papers, slideshows, posters and websites. The book provides pragmatic advice on the preparation and delivery of exceptional scientific presentations and demonstrates hundreds of visually striking presentation techniques. - Features clear headings for each section, indicating its message with graphic illustrations - Provides clear and concise explanations of design principles traditionally taught in design or visualization courses - Includes examples of high-quality figures, page layouts, slides, posters and webpages to aid readers in creating their own presentations - Includes numerous "before and after" examples to illustrate the contrast between poor and outstanding presentations




Mathematica Navigator


Book Description

Mathematica Navigator gives you a general introduction to Mathematica. The book emphasizes graphics, methods of applied mathematics and statistics, and programming. Mathematica Navigator can be used both as a tutorial and as a handbook. While no previous experience with Mathematica is required, most chapters also include advanced material, so that the book will be a valuable resource for both beginners and experienced users.