Search results for ""Author PETER BRUCE""

For over 50 years Heavy Weather Sailing has been regarded as the ultimate international authority on surviving storms at sea aboard sailing and motor vessels. In this book, former Commodore of the Ocean Cruising Club Martin Thomas brings together a wealth of expert advice from many of the great sailors of the present, including fresh accounts of yachts overtaken by extreme weather, from Ewan Southby-Tailyour, Alex Whitworth and Dag Pike to Larry and Lin Pardey, Matt Sheahan and Andrew Claughton. The expert advice section has been updated in line with current thinking, with major new additions tackling preventing or coping with lightning strikes, navigating in heavy weather with both paper and electronic charts, the choice and use of tenders in severe weather, and special problems faced by the new generation of foiled cruising boats. For the first time the book also covers the unique challenges presented by weather in high latitudes, with more yachts crossing the Drake Passage and attempting the North West Passage. These revisions ensure that Heavy Weather Sailing is as relevant, useful and instructive for today’s sailor venturing offshore as it ever was. This is the definitive book for crews of any size contemplating voyages out of sight of land anywhere in the world, whether racing or cruising. It gives a clear message regarding the preparations required, and the tactics to consider when it comes on to blow.

Read more less

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 questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data

Read more less