Data Analysis A Complete Guide 2019 Edition

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Python for Data Analysis

Author : Wes McKinney
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491957611
Total Pages : 553 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Python for Data Analysis by : Wes McKinney

Download or read book Python for Data Analysis written by Wes McKinney and published by "O'Reilly Media, Inc.". This book was released on 2017-09-25 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Making Sense of Data I

Author : Glenn J. Myatt
Publisher : John Wiley & Sons
ISBN 13 : 1118422104
Total Pages : 262 pages
Book Rating : 4.1/5 (184 download)

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Book Synopsis Making Sense of Data I by : Glenn J. Myatt

Download or read book Making Sense of Data I written by Glenn J. Myatt and published by John Wiley & Sons. This book was released on 2014-07-02 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the First Edition “...a well-written book on data analysis and data mining that provides an excellent foundation...” —CHOICE “This is a must-read book for learning practical statistics and data analysis...” —Computing Reviews.com A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors’ practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study. In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features: Updated exercises for both manual and computer-aided implementation with accompanying worked examples New appendices with coverage on the freely available TraceisTM software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches Additional real-world examples of data preparation to establish a practical background for making decisions from data Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.

Best Practices in Data Cleaning

Author : Jason W. Osborne
Publisher : SAGE
ISBN 13 : 1412988012
Total Pages : 297 pages
Book Rating : 4.4/5 (129 download)

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Book Synopsis Best Practices in Data Cleaning by : Jason W. Osborne

Download or read book Best Practices in Data Cleaning written by Jason W. Osborne and published by SAGE. This book was released on 2013 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many researchers jump straight from data collection to data analysis without realizing how analyses and hypothesis tests can go profoundly wrong without clean data. This book provides a clear, step-by-step process of examining and cleaning data in order to decrease error rates and increase both the power and replicability of results. Jason W. Osborne, author of Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are research-based and will motivate change in practice by empirically demonstrating, for each topic, the benefits of following best practices and the potential consequences of not following these guidelines. If your goal is to do the best research you can do, draw conclusions that are most likely to be accurate representations of the population(s) you wish to speak about, and report results that are most likely to be replicated by other researchers, then this basic guidebook will be indispensible.

Problem Solving and Data Analysis Using Minitab

Author : Rehman M. Khan
Publisher : John Wiley & Sons
ISBN 13 : 1118307526
Total Pages : 715 pages
Book Rating : 4.1/5 (183 download)

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Book Synopsis Problem Solving and Data Analysis Using Minitab by : Rehman M. Khan

Download or read book Problem Solving and Data Analysis Using Minitab written by Rehman M. Khan and published by John Wiley & Sons. This book was released on 2013-01-28 with total page 715 pages. Available in PDF, EPUB and Kindle. Book excerpt: Six Sigma statistical methodology using Minitab Problem Solving and Data Analysis using Minitab presents example-based learning to aid readers in understanding how to use MINITAB 16 for statistical analysis and problem solving. Each example and exercise is broken down into the exact steps that must be followed in order to take the reader through key learning points and work through complex analyses. Exercises are featured at the end of each example so that the reader can be assured that they have understood the key learning points. Key features: Provides readers with a step by step guide to problem solving and statistical analysis using Minitab 16 which is also compatible with version 15. Includes fully worked examples with graphics showing menu selections and Minitab outputs. Uses example based learning that the reader can work through at their pace. Contains hundreds of screenshots to aid the reader, along with explanations of the statistics being performed and interpretation of results. Presents the core statistical techniques used by Six Sigma Black Belts. Contains examples, exercises and solutions throughout, and is supported by an accompanying website featuring the numerous example data sets. Making Six Sigma statistical methodology accessible to beginners, this book is aimed at numerical professionals, students or academics who wish to learn and apply statistical techniques for problem solving, process improvement or data analysis whilst keeping mathematical theory to a minimum.

Bayesian Data Analysis, Third Edition

Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1439840954
Total Pages : 677 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman

Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

A Practitioner's Guide to Business Analytics (PB)

Author : Randy Bartlett
Publisher : McGraw Hill Professional
ISBN 13 : 0071807608
Total Pages : 289 pages
Book Rating : 4.0/5 (718 download)

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Book Synopsis A Practitioner's Guide to Business Analytics (PB) by : Randy Bartlett

Download or read book A Practitioner's Guide to Business Analytics (PB) written by Randy Bartlett and published by McGraw Hill Professional. This book was released on 2013-01-25 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain the competitive edge with the smart use of business analytics In today’s volatile business environment, the strategic use of business analytics is more important than ever. A Practitioners Guide to Business Analytics helps you get the organizational commitment you need to get business analytics up and running in your company. It provides solutions for meeting the strategic challenges of applying analytics, such as: Integrating analytics into decision making, corporate culture, and business strategy Leading and organizing analytics within the corporation Applying statistical qualifications, statistical diagnostics, and statistical review Providing effective building blocks to support analytics—statistical software, data collection, and data management Randy Bartlett, Ph.D., is Chief Statistical Officer of the consulting company Blue Sigma Analytics. He currently works with Infosys, where he has helped build their new Business Analytics practice.

Mastering Spark with R

Author : Javier Luraschi
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492046329
Total Pages : 296 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Mastering Spark with R by : Javier Luraschi

Download or read book Mastering Spark with R written by Javier Luraschi and published by "O'Reilly Media, Inc.". This book was released on 2019-10-07 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you’re like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems. Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems. This book covers relevant data science topics, cluster computing, and issues that should interest even the most advanced users. Analyze, explore, transform, and visualize data in Apache Spark with R Create statistical models to extract information and predict outcomes; automate the process in production-ready workflows Perform analysis and modeling across many machines using distributed computing techniques Use large-scale data from multiple sources and different formats with ease from within Spark Learn about alternative modeling frameworks for graph processing, geospatial analysis, and genomics at scale Dive into advanced topics including custom transformations, real-time data processing, and creating custom Spark extensions

Data Analysis with Microsoft Power BI

Author : Brian Larson
Publisher : McGraw Hill Professional
ISBN 13 : 1260458628
Total Pages : 546 pages
Book Rating : 4.2/5 (64 download)

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Book Synopsis Data Analysis with Microsoft Power BI by : Brian Larson

Download or read book Data Analysis with Microsoft Power BI written by Brian Larson and published by McGraw Hill Professional. This book was released on 2020-01-03 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore, create, and manage highly interactive data visualizations using Microsoft Power BI Extract meaningful business insights from your disparate enterprise data using the detailed information contained in this practical guide. Written by a recognized BI expert and bestselling author, Data Analysis with Microsoft Power BI teaches you the skills you need to interact with, author, and maintain robust visualizations and custom data models. Hands-on exercises based on real-life business scenarios clearly demonstrate each technique. Publishing your results to the Power BI Service (PowerBI.com) and Power BI Report Server are also fully covered. Inside, you will discover how to: •Understand Business Intelligence and self-service analytics •Explore the tools and features of Microsoft Power BI •Create and format effective data visualizations •Incorporate advanced interactivity and custom graphics •Build and populate accurate data models •Transform data using the Power BI Query Editor •Work with measures, calculated columns, and tabular models •Write powerful DAX language scripts •Share content on the PowerBI Service (PowerBI.com) •Store your visualizations on the Power BI Report Server

R for Data Science

Author : Hadley Wickham
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491910364
Total Pages : 521 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis R for Data Science by : Hadley Wickham

Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Statistics, Data Mining, and Machine Learning in Astronomy

Author : Željko Ivezić
Publisher : Princeton University Press
ISBN 13 : 0691151687
Total Pages : 550 pages
Book Rating : 4.6/5 (911 download)

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Book Synopsis Statistics, Data Mining, and Machine Learning in Astronomy by : Željko Ivezić

Download or read book Statistics, Data Mining, and Machine Learning in Astronomy written by Željko Ivezić and published by Princeton University Press. This book was released on 2014-01-12 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers

Introduction to Data Science

Author : Rafael A. Irizarry
Publisher : CRC Press
ISBN 13 : 1000708039
Total Pages : 836 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Introduction to Data Science by : Rafael A. Irizarry

Download or read book Introduction to Data Science written by Rafael A. Irizarry and published by CRC Press. This book was released on 2019-11-20 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

Making Sense of Data II

Author : Glenn J. Myatt
Publisher : John Wiley & Sons
ISBN 13 : 0470222808
Total Pages : 325 pages
Book Rating : 4.4/5 (72 download)

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Book Synopsis Making Sense of Data II by : Glenn J. Myatt

Download or read book Making Sense of Data II written by Glenn J. Myatt and published by John Wiley & Sons. This book was released on 2009-02-03 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on guide to making valuable decisions from data using advanced data mining methods and techniques This second installment in the Making Sense of Data series continues to explore a diverse range of commonly used approaches to making and communicating decisions from data. Delving into more technical topics, this book equips readers with advanced data mining methods that are needed to successfully translate raw data into smart decisions across various fields of research including business, engineering, finance, and the social sciences. Following a comprehensive introduction that details how to define a problem, perform an analysis, and deploy the results, Making Sense of Data II addresses the following key techniques for advanced data analysis: Data Visualization reviews principles and methods for understanding and communicating data through the use of visualization including single variables, the relationship between two or more variables, groupings in data, and dynamic approaches to interacting with data through graphical user interfaces. Clustering outlines common approaches to clustering data sets and provides detailed explanations of methods for determining the distance between observations and procedures for clustering observations. Agglomerative hierarchical clustering, partitioned-based clustering, and fuzzy clustering are also discussed. Predictive Analytics presents a discussion on how to build and assess models, along with a series of predictive analytics that can be used in a variety of situations including principal component analysis, multiple linear regression, discriminate analysis, logistic regression, and Naïve Bayes. Applications demonstrates the current uses of data mining across a wide range of industries and features case studies that illustrate the related applications in real-world scenarios. Each method is discussed within the context of a data mining process including defining the problem and deploying the results, and readers are provided with guidance on when and how each method should be used. The related Web site for the series (www.makingsenseofdata.com) provides a hands-on data analysis and data mining experience. Readers wishing to gain more practical experience will benefit from the tutorial section of the book in conjunction with the TraceisTM software, which is freely available online. With its comprehensive collection of advanced data mining methods coupled with tutorials for applications in a range of fields, Making Sense of Data II is an indispensable book for courses on data analysis and data mining at the upper-undergraduate and graduate levels. It also serves as a valuable reference for researchers and professionals who are interested in learning how to accomplish effective decision making from data and understanding if data analysis and data mining methods could help their organization.

ggplot2

Author : Hadley Wickham
Publisher : Springer Science & Business Media
ISBN 13 : 0387981411
Total Pages : 211 pages
Book Rating : 4.3/5 (879 download)

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Book Synopsis ggplot2 by : Hadley Wickham

Download or read book ggplot2 written by Hadley Wickham and published by Springer Science & Business Media. This book was released on 2009-10-03 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides both rich theory and powerful applications Figures are accompanied by code required to produce them Full color figures

A Step-by-Step Guide to Qualitative Data Coding

Author : Philip Adu
Publisher : Routledge
ISBN 13 : 1351044494
Total Pages : 460 pages
Book Rating : 4.3/5 (51 download)

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Book Synopsis A Step-by-Step Guide to Qualitative Data Coding by : Philip Adu

Download or read book A Step-by-Step Guide to Qualitative Data Coding written by Philip Adu and published by Routledge. This book was released on 2019-04-05 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Step-by-Step Guide to Qualitative Data Coding is a comprehensive qualitative data analysis guide. It is designed to help readers to systematically analyze qualitative data in a transparent and consistent manner, thus promoting the credibility of their findings. The book examines the art of coding data, categorizing codes, and synthesizing categories and themes. Using real data for demonstrations, it provides step-by-step instructions and illustrations for analyzing qualitative data. Some of the demonstrations include conducting manual coding using Microsoft Word and how to use qualitative data analysis software such as Dedoose, NVivo and QDA Miner Lite to analyze data. It also contains creative ways of presenting qualitative findings and provides practical examples. After reading this book, readers will be able to: Analyze qualitative data and present their findings Select an appropriate qualitative analysis tool Decide on the right qualitative coding and categorization strategies for their analysis Develop relationships among categories/themes Choose a suitable format for the presentation of the findings It is a great resource for qualitative research instructors and undergraduate and graduate students who want to gain skills in analyzing qualitative data or who plan to conduct a qualitative study. It is also useful for researchers and practitioners in the social and health sciences fields.

Qualitative Data Analysis with ATLAS.ti

Author : Susanne Friese
Publisher : SAGE
ISBN 13 : 1473904080
Total Pages : 333 pages
Book Rating : 4.4/5 (739 download)

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Book Synopsis Qualitative Data Analysis with ATLAS.ti by : Susanne Friese

Download or read book Qualitative Data Analysis with ATLAS.ti written by Susanne Friese and published by SAGE. This book was released on 2014-01-30 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you struggling to get to grips with qualitative data analysis? Do you need help getting started using ATLAS.ti? Do you find software manuals difficult to relate to? Written by a leading expert on ATLAS.ti, this book will guide you step-by-step through using the software to support your research project. In this updated second edition, you will find clear, practical advice on preparing your data, setting up a new project in ATLAS.ti, developing a coding system, asking questions, finding answers and preparing your results. The new edition features: methodological as well as technical advice numerous practical exercises and examples screenshots showing you each stage of analysis in version 7 of ATLAS.ti increased coverage of transcription new sections on analysing video and multimedia data a companion website with online tutorials and data sets. Susanne Friese teaches qualitative methods at the University of Hanover and at various PhD schools, provides training and consultancy for ATLAS.ti at the intersection between developers and users.

Statistics Done Wrong

Author : Alex Reinhart
Publisher : No Starch Press
ISBN 13 : 1593276206
Total Pages : 177 pages
Book Rating : 4.5/5 (932 download)

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Book Synopsis Statistics Done Wrong by : Alex Reinhart

Download or read book Statistics Done Wrong written by Alex Reinhart and published by No Starch Press. This book was released on 2015-03-01 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong. Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You'll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your peers do statistics. You'll find advice on: –Asking the right question, designing the right experiment, choosing the right statistical analysis, and sticking to the plan –How to think about p values, significance, insignificance, confidence intervals, and regression –Choosing the right sample size and avoiding false positives –Reporting your analysis and publishing your data and source code –Procedures to follow, precautions to take, and analytical software that can help Scientists: Read this concise, powerful guide to help you produce statistically sound research. Statisticians: Give this book to everyone you know. The first step toward statistics done right is Statistics Done Wrong.

Statistical Analysis Quick Reference Guidebook

Author : Alan C. Elliott
Publisher : SAGE
ISBN 13 : 9781412925600
Total Pages : 280 pages
Book Rating : 4.9/5 (256 download)

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Book Synopsis Statistical Analysis Quick Reference Guidebook by : Alan C. Elliott

Download or read book Statistical Analysis Quick Reference Guidebook written by Alan C. Elliott and published by SAGE. This book was released on 2007 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical `cut to the chase′ handbook that quickly explains the when, where, and how of statistical data analysis as it is used for real-world decision-making in a wide variety of disciplines. In this one-stop reference, the authors provide succinct guidelines for performing an analysis, avoiding pitfalls, interpreting results and reporting outcomes.