PRACTICAL STATISTICS FOR DATA SCIENTISTS

Download Practical Statistics For Data Scientists ebook PDF or Read Online books in PDF, EPUB, and Mobi Format. Click Download or Read Online button to PRACTICAL STATISTICS FOR DATA SCIENTISTS book pdf for free now.

Practical Statistics For Data Scientists

Author : Peter Bruce
ISBN : 9781491952931
Genre : Computers
File Size : 58.62 MB
Format : PDF, Mobi
Download : 370
Read : 154

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
Category: Computers

Principles Of Data Science

Author : Sinan Ozdemir
ISBN : 9781785888922
Genre : Computers
File Size : 57.92 MB
Format : PDF, Mobi
Download : 482
Read : 1176

Learn the techniques and math you need to start making sense of your data About This Book Enhance your knowledge of coding with data science theory for practical insight into data science and analysis More than just a math class, learn how to perform real-world data science tasks with R and Python Create actionable insights and transform raw data into tangible value Who This Book Is For You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you. What You Will Learn Get to know the five most important steps of data science Use your data intelligently and learn how to handle it with care Bridge the gap between mathematics and programming Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results Build and evaluate baseline machine learning models Explore the most effective metrics to determine the success of your machine learning models Create data visualizations that communicate actionable insights Read and apply machine learning concepts to your problems and make actual predictions In Detail Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means. Style and approach This is an easy-to-understand and accessible tutorial. It is a step-by-step guide with use cases, examples, and illustrations to get you well-versed with the concepts of data science. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world.
Category: Computers

Practical Statistics For Geographers And Earth Scientists

Author : Nigel Walford
ISBN : 9781119957027
Genre : Science
File Size : 66.74 MB
Format : PDF, Mobi
Download : 435
Read : 693

Practical Statistics for Geographers and Earth Scientists provides an introductory guide to the principles and application of statistical analysis in context. This book helps students to gain the level of competence in statistical procedures necessary for independent investigations, field-work and other projects. The aim is to explain statistical techniques using data relating to relevant geographical, geospatial, earth and environmental science examples, employing graphics as well as mathematical notation for maximum clarity. Advice is given on asking the appropriate preliminary research questions to ensure that the correct data is collected for the chosen statistical analysis method. The book offers a practical guide to making the transition from understanding principles of spatial and non-spatial statistical techniques to planning a series analyses and generating results using statistical and spreadsheet computer software. Learning outcomes included in each chapter International focus Explains the underlying mathematical basis of spatial and non-spatial statistics Provides an geographical, geospatial, earth and environmental science context for the use of statistical methods Written in an accessible, user-friendly style Datasets available on accompanying website at www.wiley.com/go/Walford
Category: Science

Practical Environmental Statistics And Data Analysis

Author : Yue Rong
ISBN : 9781906799045
Genre : NATURE
File Size : 67.34 MB
Format : PDF, Docs
Download : 340
Read : 769

"Describes the application of statistical methods in different environmental fields, with an emphasis on how to solve real-world problems in complex systems"--Provided by publisher.
Category: NATURE

Practical Statistics For The Analytical Scientist

Author : S. L. R. Ellison
ISBN : 9780854041312
Genre : Science
File Size : 56.41 MB
Format : PDF, ePub, Mobi
Download : 896
Read : 945

"Completely revised and updated, the second edition contains new sections on method validation, measurement uncertainty, effective experimental design and proficiency testing."--pub. desc.
Category: Science

Hands On Machine Learning With R

Author : Brad Boehmke
ISBN : 9781000730432
Genre : Business & Economics
File Size : 29.92 MB
Format : PDF, Docs
Download : 221
Read : 201

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.
Category: Business & Economics

Practical Data Science With R

Author : Nina Zumel
ISBN : 1617295876
Genre : Computers
File Size : 84.38 MB
Format : PDF, Mobi
Download : 591
Read : 336

This invaluable addition to any data scientist's library shows you how to apply the R programming language and useful statistical techniques to everyday business situations as well as how to effectively present results to audiences of all levels. To answer the ever-increasing demand for machine learning and analysis, this new edition boasts additional R tools, modeling techniques, and more. Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever-expanding field of data science. You'll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Category: Computers

Network Security Through Data Analysis

Author : Michael Collins
ISBN : 9781491962794
Genre : Computers
File Size : 36.77 MB
Format : PDF, Mobi
Download : 496
Read : 379

Traditional intrusion detection and logfile analysis are no longer enough to protect today’s complex networks. In the updated second edition of this practical guide, security researcher Michael Collins shows InfoSec personnel the latest techniques and tools for collecting and analyzing network traffic datasets. You’ll understand how your network is used, and what actions are necessary to harden and defend the systems within it. In three sections, this book examines the process of collecting and organizing data, various tools for analysis, and several different analytic scenarios and techniques. New chapters focus on active monitoring and traffic manipulation, insider threat detection, data mining, regression and machine learning, and other topics. You’ll learn how to: Use sensors to collect network, service, host, and active domain data Work with the SiLK toolset, Python, and other tools and techniques for manipulating data you collect Detect unusual phenomena through exploratory data analysis (EDA), using visualization and mathematical techniques Analyze text data, traffic behavior, and communications mistakes Identify significant structures in your network with graph analysis Examine insider threat data and acquire threat intelligence Map your network and identify significant hosts within it Work with operations to develop defenses and analysis techniques
Category: Computers

Practical Statistics For Environmental And Biological Scientists

Author : John Townend
ISBN : 9781118687413
Genre : Science
File Size : 35.32 MB
Format : PDF, ePub
Download : 130
Read : 285

All students and researchers in environmental and biologicalsciences require statistical methods at some stage of their work.Many have a preconception that statistics are difficult andunpleasant and find that the textbooks available are difficult tounderstand. Practical Statistics for Environmental and BiologicalScientists provides a concise, user-friendly, non-technicalintroduction to statistics. The book covers planning and designingan experiment, how to analyse and present data, and the limitationsand assumptions of each statistical method. The text does not referto a specific computer package but descriptions of how to carry outthe tests and interpret the results are based on the approachesused by most of the commonly used packages, e.g. Excel, MINITAB andSPSS. Formulae are kept to a minimum and relevant examples areincluded throughout the text.
Category: Science