Practical Statistics For Data Scientists

Author: Peter Bruce
Publisher: "O'Reilly Media, Inc."
ISBN: 1491952938
Size: 15.80 MB
Format: PDF, ePub, Docs
View: 522
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various 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 programming language, 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

Statistical Methods For Machine Learning

Author: Jason Brownlee
Publisher: Machine Learning Mastery
Size: 31.50 MB
Format: PDF
View: 6326
Statistics is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. Cut through the equations, Greek letters, and confusion, and discover the topics in statistics that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, and much more.

Data Mining For Business Analytics

Author: Galit Shmueli
Publisher: John Wiley & Sons
ISBN: 1119549841
Size: 25.42 MB
Format: PDF, ePub, Mobi
View: 186
Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

Statistics For Data Science

Author: James D. Miller
Publisher: Packt Publishing Ltd
ISBN: 178829534X
Size: 31.54 MB
Format: PDF, Kindle
View: 3226
Get your statistics basics right before diving into the world of data science About This Book No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; Implement statistics in data science tasks such as data cleaning, mining, and analysis Learn all about probability, statistics, numerical computations, and more with the help of R programs Who This Book Is For This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful. What You Will Learn Analyze the transition from a data developer to a data scientist mindset Get acquainted with the R programs and the logic used for statistical computations Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks Get comfortable with performing various statistical computations for data science programmatically In Detail Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically. Style and approach Step by step comprehensive guide with real world examples

Essential Statistics For The Pharmaceutical Sciences

Author: Philip Rowe
Publisher: John Wiley & Sons
ISBN: 047003470X
Size: 11.63 MB
Format: PDF, Mobi
View: 6815
"... this text takes a novel approach... The style... is not as dry as other statistics texts, and so should not be intimidating even to a relative newcomer to the subject... The layout is easy to navigate, there are chapter aims, summaries and “key point boxes” throughout." -The Pharmaceutical Journal, 2008 This text is a clear, accessible introduction to the key statistical techniques employed for the analysis of data within this subject area. Written in a concise and logical manner, the book explains why statistics are necessary and discusses the issues that experimentalists need to consider. The reader is carefully taken through the whole process, from planning an experiment to interpreting the results, avoiding unnecessary calculation methodology. The most commonly used statistical methods are described in terms of their purpose, when they should be used and what they mean once they have been performed. Numerous examples are provided throughout the text, all within a pharmaceutical context, with key points highlighted in summary boxes to aid student understanding. Essential Statistics for the Pharmaceutical Sciences takes a new and innovative approach to statistics with an informal style that will appeal to the reader who finds statistics a challenge! This book is an invaluable introduction to statistics for any science student. It is an essential text for students taking biomedical or pharmaceutical-based science degrees and also a useful guide for researchers.

Library Journal

Size: 29.45 MB
Format: PDF
View: 979
Includes, beginning Sept. 15, 1954 (and on the 15th of each month, Sept.-May) a special section: School library journal, ISSN 0000-0035, (called Junior libraries, 1954-May 1961). Also issued separately.

Nuclear Computational Science

Author: Yousry Azmy
Publisher: Springer Science & Business Media
ISBN: 9789048134113
Size: 75.55 MB
Format: PDF, ePub
View: 6268
Nuclear engineering has undergone extensive progress over the years. In the past century, colossal developments have been made and with specific reference to the mathematical theory and computational science underlying this discipline, advances in areas such as high-order discretization methods, Krylov Methods and Iteration Acceleration have steadily grown. Nuclear Computational Science: A Century in Review addresses these topics and many more; topics which hold special ties to the first half of the century, and topics focused around the unique combination of nuclear engineering, computational science and mathematical theory. Comprising eight chapters, Nuclear Computational Science: A Century in Review incorporates a number of carefully selected issues representing a variety of problems, providing the reader with a wealth of information in both a clear and concise manner. The comprehensive nature of the coverage and the stature of the contributing authors combine to make this a unique landmark publication. Targeting the medium to advanced level academic, this book will appeal to researchers and students with an interest in the progression of mathematical theory and its application to nuclear computational science.

Influenza Models

Author: P. Selby
Publisher: Springer Science & Business Media
ISBN: 9401180504
Size: 23.28 MB
Format: PDF, Mobi
View: 928
Kilbourne (1973) described the student of influenza as "continually looking back over his shoulder and asking 'what happened?', in the hope that understanding of past events will alert him to the catastrophies ofthe future". Experience suggests the futility of such a hope, since the most predictable feature of influenza is its unpredictability. Nonetheless, the stubborn viabil ity of this hope is strongly affirmed by the many attempts, described and discussed in this volume, to develop a useful and practical representation of influenza virus behavior. I hasten to add, however, that the desired model has yet to be perfected. The existence and usefulness of animal models of infectious diseases of man are well documented. Reproduction of disease by infecting an experimental animal satisfies the third of Koch's four postulates to establish proof of disease causation by a specific bacterium. Animal models also have been extremely useful in studies of the pathogenesis, immunoprophylaxis, and specific therapy of several important diseases, ineluding (with only modest success) influenza. Development of such a model is simple, at least in concept. and can be achieved by one or only a few scientists.