| title | excerpt | publishDate |
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The Best Data Analysis Books |
Explore top-rated data analysis books, perfect for professionals and beginners seeking to master the art of data-driven decision-making. Discover the latest and best-selling titles offering practical insights and actionable strategies in this captivating roundup. |
2024-05-18T09:49:40Z |
If you're on the quest for knowledge in the realm of data analysis, look no further! This book roundup article, titled "Data Analysis Books, " is your comprehensive guide to the top reads in this field. We've curated a selection of must-reads that will equip you with the skills necessary to navigate and thrive in data analysis. Step into the world of big data and explore the insights and advancements contained within these literary treasures.
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Discover the fascinating world of statistics with "The New Statistical Analysis of Data" by renowned authors T. W. Anderson and Jeremy Finn. This comprehensive guide, published by Springer Science & Business Media, is a must-read for students and professionals alike, offering a non-calculus based introduction to the subject.
Covering a wide range of topics, this book provides an in-depth analysis of statistical techniques and methods, offering numerous examples drawn from real-life research studies. The authors' expertise shines through as they simplify complex concepts, providing intuitive explanations through illustrative figures and numerical examples.
"The New Statistical Analysis of Data" explores the diverse applications of statistical methods in fields such as business, engineering, health sciences, social sciences, and education. The book delves into descriptive statistics, exploring the organization and summarization of data, before delving into more advanced topics like probability, statistical inference, and advanced statistical techniques.
With a total of 742 pages, this informative resource is perfect for anyone seeking to gain a solid understanding of statistical analysis. It's the perfect companion for students and professionals alike, requiring minimal mathematical background and offering a wealth of knowledge in an accessible format. Don't miss out on this essential guide to the statistical world - get your copy of "The New Statistical Analysis of Data" today!
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Authors: T.W. Anderson, Jeremy Finn
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Publisher: Springer Science & Business Media
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Published Date: December 13, 1996
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Page Count: 742
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Print Type: BOOK
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Categories: Mathematics
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Maturity Rating: NOT_MATURE
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Language: en
"Statistical Data Analysis" by Glen Cowan delves into the practical application of statistics in the realm of data analysis, particularly within the physical sciences. This comprehensive guide is designed for students and professionals alike, aiding them in drawing reliable quantitative conclusions from experimental data.
Despite its primary focus on particle physics, Cowan's work is presented in a manner that caters to a broader audience, making it useful for individuals from various branches of the physical sciences. The book begins by providing readers with a solid foundation in data analysis, covering essential concepts such as probability and random variables, Monte Carlo techniques, statistical tests, and parameter estimation.
As the reader progresses, they are introduced to more specialized topics in the field. These include interval estimation, characteristic functions, and the intricate problem of correcting distributions for the impact of measurement errors (unfolding).
By bridging the gap between theory and practical application, "Statistical Data Analysis" offers a valuable resource for those seeking to deepen their understanding of data analysis in the physical sciences.
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Authors: Glen Cowan
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Publisher: Oxford University Press
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Published Date: May 17, 1998
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Page Count: 218
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Print Type: BOOK
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Categories: Mathematics
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Maturity Rating: NOT_MATURE
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Language: en
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A Practical Guide to Exploratory Data Analysis and Data Mining
Making Sense of Data is a comprehensive guide dedicated to demystifying the world of data analysis and data mining. Created by the esteemed author, Glenn J. Myatt, this book takes a practical and step-by-step approach to educating readers about the intricacies of completing successful data analysis or data mining projects, making it invaluable to professionals in virtually any field.
The book begins by breaking down the key elements and issues that need to be taken into consideration, while providing lucid explanations to enable the reader to make essential, timely, and accurate decisions from data. Through a methodical and well-structured approach, readers learn how to analyze and implement data analysis results effectively, ultimately leading to the formation of more intelligent business decisions.
Moreover, this valuable resource is filled with an extensive collection of methods from both data analysis and data mining disciplines, enabling readers to solve a wide range of problems. It does so by addressing the necessary considerations, outlining the crucial steps, and handling technical topics professionally, ensuring effective decision-making from data.
Equipped with comprehensive discussions on various essential topics like problem definitions, data preparation, data visualization, data mining, statistics, grouping methods, predictive modeling, and deployment issues and applications, readers are given a solid foundation for tackling complex data analysis or data mining projects.
In summary, Making Sense of Data is a must-read for anyone seeking to deepen their understanding of data analysis and mining. By examining the reasons behind the multiple approaches needed, addressing the issues that arise, and providing a detailed outline of the processes and methods involved, this book empowers readers to approach data analysis and mining with confidence and precision.
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Authors: Glenn J. Myatt
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Publisher: John Wiley & Sons
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Published Date: February 26, 2007
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Page Count: 294
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Print Type: BOOK
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Categories: Mathematics
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Average Rating: 4.5
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Ratings Count: 2.0
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Maturity Rating: NOT_MATURE
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Language: en
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Discover the secrets of mastering data analysis with this comprehensive guide! Published by Cambridge University Press in 2007, "Data Analysis Using Regression and Multilevel/Hierarchical Models" is an indispensable resource for applied researchers seeking to analyze linear and nonlinear regression, as well as multilevel models, in their data-driven projects. With 654 pages packed with insightful information, this book breaks down complex techniques into practical, easy-to-understand concepts.
Don't miss your chance to dive into the world of advanced data analysis with "Data Analysis Using Regression and Multilevel/Hierarchical Models. ".
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Authors: Andrew Gelman, Jennifer Hill
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Publisher: Cambridge University Press
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Published Date: May 17, 2007
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Page Count: 654
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Print Type: BOOK
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Categories: Mathematics
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Average Rating: 5.0
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Ratings Count: 2.0
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Maturity Rating: NOT_MATURE
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Language: en
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Step into the spellbinding realm of correlated data analysis with the esteemed author Xue-Kun Song. This book, boasting an extensive analysis of modeling, analytics, and applications, is a crucial read for anyone eager to expand their comprehension in this niche area.
The author utilizes a revolutionary dispersion model class to systematically dissect joint models for correlated data. This method broadens the scope of data types it can accommodate, surpassing the restrictions imposed by conventional generalized linear models.
In addition to exploring marginal and mixed-effects models, the book also delves into the fascinating subject of joint regression analysis, which employs Gaussian copulas as its base. For those who wish to delve deeper into the intricacies of estimating functions and are interested in GEE and QIF as particular instances, this book serves as a splendid reference point.
Containing a total of 356 pages, this groundbreaking publication is published by Springer Science & Business Media and is accessible in print format. Do not miss the opportunity to immerse yourself in the engaging world of correlated data analysis and elevate your mathematical expertise with this indispensable source.
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Authors: Xue-Kun Song
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Publisher: Springer Science & Business Media
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Published Date: July 27, 2007
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Page Count: 356
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Print Type: BOOK
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Categories: Mathematics
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Maturity Rating: NOT_MATURE
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Language: en
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A Hands-On Guide for Programmers and Data Scientists
Discover the power of data analysis with Open Source Tools, a hands-on guide for programmers and data scientists. This intriguing book takes you on a journey to collect and transform raw information into something valuable for your business environment. Developed by Philipp K. Janert, the book delves into techniques for data exploration, conceptual modeling, and data visualization. It also covers advanced topics such as mining data, scaling and probability arguments, and predictive analytics.
Open Source Tools provides a comprehensive overview of different open-source programming environments for data analysis. You'll learn how to make your conclusions understandable through reports, dashboards, and other metrics programs, and understand financial calculations like the time-value of money. This book is a must-read for those aiming to become data scientists, with positive reviews from professionals in the field.
The concise descriptions, hands-on workshops, and engaging exercises in Open Source Tools allow learners to truly grasp the essence of data analysis. With a focus on thinking about the results you want to achieve rather than relying on tools, this book empowers you to conquer even the most challenging data analysis situations. Don't miss out on this essential guide to unlocking the potential of your data.
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Authors: Philipp K. Janert
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Publisher: "O'Reilly Media, Inc."
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Published Date: November 11, 2010
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Page Count: 540
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Print Type: BOOK
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Categories: Computers
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Average Rating: 4.0
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Ratings Count: 1.0
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Maturity Rating: NOT_MATURE
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Language: en
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How to Answer Almost Any Question Using Basic Statistics
The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics offers a unique and engaging approach to understanding statistics in the real world. Written by author Kristin H. Jarman, this comprehensive guide takes readers through the entire data collection and analysis process while emphasizing the importance of critical thinking.
Accessible whether you're a student, manager, or researcher, this book tackles essential concepts in data analysis such as descriptive statistics, analysis of variance, probability, confidence intervals, hypothesis tests, regression, and statistical correlation. Instead of focusing on dry mathematic proofs, Jarman presents a fun and interesting perspective on statistics that is useful for everyday life. With light-hearted case studies and real-life applications, The Art of Data Analysis is sure to captivate readers and provide an intuitive understanding of basic statistical reasoning.
Published by John Wiley & Sons, this 190-page book is an excellent addition to any statistics library and an indispensable resource for anyone seeking to answer almost any question using basic statistics.
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Authors: Kristin H. Jarman
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Publisher: John Wiley & Sons
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Published Date: April 17, 2013
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Page Count: 190
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Print Type: BOOK
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Categories: Mathematics
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Maturity Rating: NOT_MATURE
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Language: en
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Dive into the world of data analysis with the Practical Data Analysis book. This comprehensive guide is designed to provide small business users and home computer enthusiasts with a practical and straightforward approach to data manipulation.
Each chapter introduces key themes of data analysis, swiftly followed by immersive practical lessons. With just 360 pages, you can quickly learn all aspects of data analysis, leaving no stone unturned.
Published by Packt Publishing Ltd in 2013, Practical Data Analysis is penned by author Hector Cuesta. This 360-page masterpiece is meticulously composed to help you master all the ins and outs of data analysis in no time, without the need for advanced technical knowledge.
The Publisher, Packt Publishing Ltd, has produced numerous books in the computer category, demonstrating their expertise. Their commitment to quality can be seen through their high-standard publications, including Practical Data Analysis.
This book is available in print and adheres strictly to Google search guidelines, ensuring a seamless user experience. As such, its page count, authors, average ratings, language and maturity rating are all readily available for those who desire more in-depth information.
In summary, the Practical Data Analysis book offers a hands-on learning experience for those eager to slice and dice data effectively and efficiently. With its easy-to-understand approach and 360 pages of practical lessons, this book is a must-read for data analysis novices.
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Authors: Hector Cuesta
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Publisher: Packt Publishing Ltd
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Published Date: October 22, 2013
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Page Count: 360
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Bayesian Data Analysis, Third Edition, serves as the definitive resource for Bayesian methods in data analysis. This classic book, known for its practical approach and accessibility, has been updated in its third edition with four new chapters on nonparametric modeling, a discussion of weakly informative priors and boundary-avoiding priors, and enhanced coverage of cross-validation and predictive information criteria.
The book also presents new and revised software code and improvements in convergence monitoring and effective sample size calculations. Written by accomplished statisticians, it provides an in-depth understanding of Bayesian inference and serves as an essential tool for undergraduate and graduate students studying Bayesian modeling and computation, as well as researchers in applied statistics.
The publication provides valuable additional materials, data sets used in examples, solutions to selected exercises, and software instructions on the book's webpage.
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Authors: Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
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Publisher: CRC Press
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Published Date: November 01, 2013
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Page Count: 677
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Print Type: BOOK
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Categories: Mathematics
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Average Rating: 5.0
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Ratings Count: 1.0
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Maturity Rating: NOT_MATURE
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Language: en
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Functional Data Analysis: A Comprehensive Guide to Unlocking the Hidden Depths of Data
Do You Crave a Deeper Understanding of your Data?
Functional Data Analysis, by renowned authors James Ramsay and B. W. Silverman, is your ultimate guide to unlocking the hidden depths of data. This groundbreaking book unveils the cutting-edge field of Functional Data Analysis, providing you with the tools you need to make sense of complex data sets.
Packed with real-world examples, this book takes you on a journey through classic techniques in linear regression, principal components analysis, linear modeling, and canonical correlation analysis, along with innovative techniques such as curve registration and principal differential analysis.
Get Ready to Embrace the Future of Data Analysis
Functional Data Analysis offers a smooth and intuitive approach that allows you to see new patterns and insights in your data. Whether you're a student, applied data analyst, or experienced researcher, this book has something for everyone. Its wide-ranging examples from fields like growth analysis, meteorology, biomechanics, equine science, economics, and medicine ensure that the concepts presented are as versatile as the data they represent.
This book expertly balances the mathematical foundations of Functional Data Analysis with practical applications, making it a highly accessible resource for any reader. With its thorough coverage of novel statistical technology, Functional Data Analysis is sure to be an indispensable tool for anyone seeking to explore the world of data analysis.
Join the Ranks of the Best Data Analysists
Don't miss this opportunity to be a part of the exciting evolution of data analysis. Whether you're new to the field or an experienced researcher, Functional Data Analysis will provide you with the knowledge and skills you need to stay ahead of the curve. Get ready to unravel the mysteries of your data and make a lasting impact on the world of statistics and beyond!
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Authors: James Ramsay, B. W. Silverman
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Publisher: Springer Science & Business Media
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Published Date: November 11, 2013
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Page Count: 317
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Print Type: BOOK
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Categories: Mathematics
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Maturity Rating: NOT_MATURE
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Language: en
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"Basic Data Analysis for Time Series with R" is a comprehensive guide that tackles the intricacies of time series data analysis with R. Authored by DeWayne R. Derryberry, this resource is an essential tool for researchers, professionals, or students seeking to gain a solid grasp on serial data analysis techniques.
Written in a reader-friendly style, this book delves into the mathematical significance of analyzing data collected in increments of time or space. It masterfully balances theory and practice, emphasizing the importance of collaborative data analysis. The book's focus lies on serial correlation and presents a coherent and systematic regression-based approach to model selection.
"Basic Data Analysis for Time Series with R" uses a range of information criteria, cross-validation, hypothesis tests, and confidence intervals to illustrate these principles of model selection and model building. It covers frequency- and time-domain and trigonometric regression as primary themes and provides modern topical coverage on Fourier series and Akaike's Information Criteria (AIC).
Real-world examples, numerous R software subroutines with graphical displays, exercise sets intended to support understanding of core concepts, and specific chapters analyzing Wolf sunspot and Vostok ice core data sets are some of the added features that make this book stand out.
With 320 pages and categorized under Mathematics, "Basic Data Analysis for Time Series with R" is a comprehensive reference book filled with knowledge and practical applications in time series data analysis using R.
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Authors: DeWayne R. Derryberry
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Publisher: John Wiley & Sons
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Published Date: June 23, 2014
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Page Count: 320
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Print Type: BOOK
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Categories: Mathematics
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Maturity Rating: NOT_MATURE
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Language: en
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A Practical Guide to Exploratory Data Analysis and Data Mining
Get ready to dive into the fascinating world of data analysis with Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition by Glenn J. Myatt and Wayne P. Johnson! This essential book is a must-read for anyone seeking to make smarter business decisions through the power of data.
The authors' extensive practical experience in data analysis and data mining shines through, offering actionable insights and clear explanations for readers from various disciplines. With a step-by-step approach, this book serves as a reliable guide, walking you through the process of carefully analyzing data and implementing its findings to foster informed decision-making.
Making Sense of Data I also comes equipped with valuable tools to help you summarize and interpret data to master the art of data analysis. In the Second Edition, you'll find an array of updated exercises for both manual and computer-aided implementation, accompanied by detailed worked examples to enhance your understanding.
Furthermore, the Second Edition provides new coverage on multiple linear regression, logistic regression, and data preparation. These wide-ranging approaches ensure that you have the necessary knowledge to tackle real-world data analysis challenges effectively.
Whether you're a researcher, professional, or seeking knowledge for an undergraduate or graduate course in data analysis or data mining, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is the perfect resource to help you make sense of data and make smarter decisions in your field.
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Authors: Glenn J. Myatt, Wayne P. Johnson
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Publisher: John Wiley & Sons
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Published Date: July 02, 2014
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Page Count: 320
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Print Type: BOOK
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Categories: Mathematics
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Maturity Rating: NOT_MATURE
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Language: en
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Embark on a journey to master data analysis with R, the world's most powerful statistical programming language. This comprehensive guide, "Data Analysis with R, " is designed for learners of all levels, from beginners to experienced professionals. With an intuitive and friendly approach, the book takes you through the basics of R, statistical reasoning, and advanced analytics techniques, all while providing practical examples and exercises.
As you delve into the fascinating world of data analysis, you'll learn how to navigate the R environment, describe and visualize data, and apply hypothesis tests. Discover the power of Bayesian methods for estimating parameters and regression for predicting continuous variables. Gain valuable insights into handling missing data and managing problematic data points. Moreover, you'll learn how to employ parallelization and Rcpp to scale your analyses to larger datasets.
"Data Analysis with R" is engineered to be an invaluable resource in any stage of your career as a data analyst. Join Tony Fischetti on this enlightening journey, where you'll not only learn the fundamental concepts but also apply them in real-world scenarios. With over 388 pages, this engaging book covers everything you need to know to become a skilled data analyst using R.
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Authors: Tony Fischetti
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Publisher: Packt Publishing Ltd
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Published Date: December 22, 2015
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Page Count: 388
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Discover the world of data analysis in life sciences with Rafael A. Irizarry and Michael I.
Love's comprehensive guide, "Data Analysis for the Life Sciences with R. " This engaging book delves into the essential statistical concepts and data analytic skills desired for a thriving career in life sciences research. From basic p-values to intricate analysis of high-throughput data, the authors guide readers through the process of data-driven life science research.
With R code included and connected to the mathematical and statistical concepts, this book is a must-have for any aspiring life scientist seeking to excel in their field. Prepare for an exhilarating journey through the intricacies of life science data analysis in "Data Analysis for the Life Sciences with R. ".
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Authors: Rafael A. Irizarry, Michael I. Love
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Publisher: CRC Press
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Published Date: October 04, 2016
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Page Count: 461
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Print Type: BOOK
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Categories: Mathematics
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Maturity Rating: NOT_MATURE
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Language: en
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Data Analysis, Visualization, and Modelling for the Data Scientist
Embark on a journey to becoming a skilled data scientist with Beginning Data Science in R: Data Analysis, Visualization, and Modeling for the Data Scientist. Written by Thomas Mailund, this comprehensive guide offers a detailed introduction to effective data manipulation, visualization, and R programming for data science.
Beginning Data Science in R emphasizes best practices for data analysis and software development while exploring the art of extracting valuable patterns from raw data. The book provides in-depth knowledge on computational methods and programming, using R as the ideal programming language to build mathematical models.
Drawing from the author's expertly crafted lecture notes, Mailund delves into topics such as data visualization and handling large datasets, building an R package, and using version control. Perfect for data science enthusiasts with experience in analytics but new to R, this insightful book caters to those eager to level up their skills and knowledge in data manipulation, visualization, and computational programming.
With a rich page count of 369 for ample learning, Beginning Data Science in R is designed to help you master data science techniques and advance your career in this rapidly evolving field. Don't miss your chance to become a proficient data scientist, armed with the essential skills and knowledge to conquer the world of data analysis and processing.
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Authors: Thomas Mailund
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Publisher: Apress
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Published Date: March 09, 2017
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Page Count: 369
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Data Wrangling with Pandas, NumPy, and IPython
Journey into the realm of data analysis with Python for Data Science - a revised, all-inclusive manual that demonstrates the art of handling, processing, cleaning, and scrutinizing datasets using pandas, NumPy, and IPython. Penned by Wes McKinney, the brainchild behind the Python pandas project, this second edition features a plethora of workable case studies and practical examples, making it the ideal companion for both novice data analysts learning Python and Python developers acquainting themselves with data science and computational science.
With a focus on the most recent versions of these influential tools, readers will gain proficiency in tackling a multitude of data analysis conundrums using tools such as Jupyter notebooks and the IPython shell.
From importing and tidying data to constructing enlightening visualizations and analyzing temporal data series, this thorough introduction to data science with Python prepares you to handle real-world data analytical dilemmas with relative ease.
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Authors: Wes McKinney
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Publisher: "O'Reilly Media, Inc."
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Published Date: September 25, 2017
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Page Count: 676
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Print Type: BOOK
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Categories: Computers
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Average Rating: 5.0
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Ratings Count: 1.0
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Maturity Rating: NOT_MATURE
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Language: en
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An Introduction to Categorical Data Analysis, Third Edition, is a comprehensive guide to statistical methods for categorical data, a vital area with significant applications in the biomedical and social sciences. This edition builds on the previous ones and adds an R software approach, a new chapter on alternative methods, new sections introducing the Bayesian approach, and over 70 real-world example data sets for better understanding.
Written in an applied, non-technical style, the book brings methodology to life using a diverse range of real data sets, including medical clinical trials, environmental inquiries, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and more. Ideal for statisticians, biostatisticians, methodologists, and professionals in social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences, this engaging book is an invaluable tool to understand and apply categorical data analysis.
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Authors: Alan Agresti
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Publisher: John Wiley & Sons
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Published Date: October 11, 2018
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Page Count: 400
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Print Type: BOOK
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Categories: Mathematics
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Maturity Rating: NOT_MATURE
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Language: en
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Quantitative, Qualitative and Mixed Methods
"An Introduction to Data Analysis: Quantitative, Qualitative and Mixed Methods" provides readers with an all-encompassing guide to the intricate world of data analysis. This thoroughly researched, jargon-free resource will enable you to unravel the complexities of data, its nuances, and relationships both within academic research and the popular media.
Beginning with a comprehensive insight into the general process of data analysis, the book moves on toward the exploration of real-world case studies that illuminate the theories behind conducting quantitative, qualitative, and mixed methods data analysis. Whether you're a novice or an expert, this comprehensive guide will equip you with the necessary tools and techniques to interpret data in a sophisticated manner.
Key topics covered include data analysis frameworks, assessing the validity and credibility of data, efficient sampling techniques, data management, the phenomenon of big data, effective data visualisation, and effective communication of results.
With their combined expertise, the authors ensure the content is straightforward to comprehend, allowing even the most inexperienced data analysts to follow along with ease.
This 381-page book, published by SAGE on October 15th, 2018, serves as an invaluable quick-reference guide for professionals, students, and individuals interested in data analysis, regardless of their levels of proficiency or background in the field.
"An Introduction to Data Analysis: Quantitative, Qualitative and Mixed Methods" not only educates but also fosters critical thinking skills, preparing you for the analytical demands of today's technological advanced world. For any individual looking for a guide that provides not just knowledge, but a well-rounded holistic approach to the understanding of data analysis, this is the book for you.
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Authors: Tiffany Bergin
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Publisher: SAGE
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Published Date: October 15, 2018
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Page Count: 381
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Print Type: BOOK
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Categories: Social Science
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Maturity Rating: NOT_MATURE
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Language: en
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Data Analysis and Prediction Algorithms with R
Dive into the captivating world of data science with 'Introduction to Data Science: Data Analysis and Prediction Algorithms with R', an essential textbook for beginners. Written by expert Rafael A. Irizarry, this comprehensive guide covers the must-know fundamentals of data analysis, predictive algorithms, and R programming.
The book is meticulously structured into six parts, each teeming with real-world case studies that serve as a mirror to a data scientist's experience. From self-reported student heights to the impact of vaccines on infectious disease rates, these case studies challenge readers to solve pressing problems using data analysis.
Learn the intricacies of R programming, data visualization, and data wrangling, among others. The author guides readers through each concept with care, ensuring they grasp every bit of the knowledge needed to become a proficient data scientist. Even if you lack prior R knowledge, the book's clear explanations make it a breeze to catch up.
However, to delve deeper, a background in probability and statistics would be advantageous. The book provides a basic introduction to these concepts, but for a more thorough understanding, it's recommended to supplement your learning with additional texts.
With 'Introduction to Data Science', you'll be ready to face the challenges of the real world, armed with the tools and knowledge of a true data expert. Are you ready to transform data into insight?
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Authors: Rafael A. Irizarry
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Publisher: CRC Press
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Published Date: November 20, 2019
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Page Count: 794
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Print Type: BOOK
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Categories: Mathematics
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Maturity Rating: NOT_MATURE
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Language: en
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Solve business problems with state-of-the-art data analysis models, developing expert data analysis skills along the way
Unleash the power of data with Python! 'The Data Analysis Workshop' is your ultimate guide to turning unstructured data into valuable insights and solving pressing business problems. Written by industry experts, Gururajan Govindan, Shubhangi Hora, and Konstantin Palagachev, this book takes you on an exciting journey through real-world use cases and cutting-edge data analysis techniques.
From a bike rental shop to a hydraulic system, you'll explore a variety of scenarios, applying advanced data analysis techniques such as hypothesis testing, correlation, and null-value imputation. You'll learn how to join and merge databases, prepare data for analysis, and handle imbalanced data, all while mastering Python models.
Packed with practical examples and expert guidance, 'The Data Analysis Workshop' will not only deepen your understanding of data analysis but also equip you with the skills to make insightful business decisions. Discover the potential of data and join the ranks of confident data analysts today!
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Authors: Gururajan Govindan, Shubhangi Hora, Konstantin Palagachev
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Publisher: Packt Publishing Ltd
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Published Date: July 29, 2020
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Page Count: 625
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Unlocking the true nature of interpersonal phenomena, Dyadic Data Analysis provides advanced, cutting-edge solutions for investigating the intricate processes that unfold between two individuals. Delve into the complexities of attachment, conflict, person perception, learning, and influence, as this comprehensive guide takes you on a journey through the behavioral and social sciences.
Perfect for researchers in psychology or any related field, this book offers exceptional methodologies and data-analytical approaches tailored for studying the interactions between couples, coworkers, parent-child, teacher-student, doctor-patient, and more. Guided by rich, diverse examples, you'll learn to conceptualize relationship processes, model and test for actor effects, partner effects, and relationship effects – all while accounting for the statistical interdependence that lies beneath.
With a companion website offering valuable supplementary materials, clarifications, elaborations, corrections, and data files, Dyadic Data Analysis ensures a well-rounded and practical learning experience. Written by esteemed experts, David A. Kenny, Deborah A. Kashy, and William L. Cook, this seminal work is an essential guide for any researcher looking to unravel the mysteries of the dyadic interpersonal world.
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Authors: David A. Kenny, Deborah A. Kashy, William L. Cook
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Publisher: Guilford Publications
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Published Date: November 26, 2020
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Page Count: 482
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Print Type: BOOK
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Categories: Psychology
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Maturity Rating: NOT_MATURE
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Language: en
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"Data Analysis for Business, Economics, and Policy" by Gábor Békés and Gábor Kézdi is a must-read for those looking to dip their toes into the world of data analysis. This 741-page Cambridge University Press publication, first released in 2021, is an essential guide for students and professionals alike who wish to navigate the labyrinthine waters of corporate strategy, economic forecasting, and public policy formulation.
Its comprehensive approach makes it a veritable treasure trove of information, aided and abetted by case studies with real-world data. So, whether you're keen on developing strategies to boost business productivity or grappling with the complexities of public policy-making, this book is your reliable companion. Don't be daunted by its sheer volume - it's more than just a book, it's an invaluable asset for anyone with a foot in the business, economics or public policy world.
As for the authors, Gábor Békés and Gábor Kézdi are industry veterans whose expertise shines through every page of this remarkable work. Let's dive into the world of data analysis and unravel its mysteries with the help of this remarkable book.
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Authors: Gábor Békés, Gábor Kézdi
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Publisher: Cambridge University Press
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Published Date: May 06, 2021
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Page Count: 741
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Print Type: BOOK
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Categories: Business & Economics
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Maturity Rating: NOT_MATURE
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Language: en
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A Friendly and Practical Introduction
"Data Analysis for Social Science: A Friendly and Practical Introduction" is a must-read for those looking to embark on the journey of data analysis in the realm of social sciences. This book, written by Elena Llaudet and Kosuke Imai, serves as a more accessible, shorter version of Imai's popular "Quantitative Social Science" book. With the focus on non-specialist undergraduate audiences, it provides an intuitive and easy-to-understand approach to data analysis using real-life data and the programming language 'R'.
From simple to complex data analysis, this book gradually progresses towards multivariate linear regression models. Emphasis is placed on understanding the concepts rather than just memorizing the formulas. The book comes with a variety of visual aids such as diagrams, cartoons, and pictures to help students grasp the complexities of the subject. Moreover, it offers a step-by-step template to conduct data analysis from start to finish, accompanied by supplemental materials both for students and instructors.
"Data Analysis for Social Science" is a comprehensive primer that allows readers to delve into the intricacies of data analysis while demystifying the process through its practical approach. Written in an engaging and accessible style, it's a perfect starting point for anyone interested in understanding the role of data analysis in the social sciences.
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Authors: Elena Llaudet, Kosuke Imai
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Publisher: Princeton University Press
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Published Date: November 29, 2022
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Page Count: 256
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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In the rapidly growing world of data analysis, R programming has become a go-to tool for organizations across diverse fields. This Beginner's Guide for Data Analysis using R Programming by Jeeva Jose is a must-read for students, professionals, and anyone interested in delving into the world of data science.
Whether you're in mathematics, statistics, physics, chemistry, biology, social science, medicine, business, engineering, software, information technology, sales, bioinformatics, pharmacy, or any discipline where data analysis is crucial – this book has got you covered. Written for readers with zero prior knowledge of R programming, the book takes you on a step-by-step journey into understanding and utilizing R for data analysis tasks.
With a simple yet powerful language, the book teaches you how to analyze, represent, and make sense of your data. As more organizations turn to R for reporting, analyzing, and visualizing data, keeping up with the trend is crucial. This guide not only introduces R programming but also showcases its potential in helping you become an essential part of the data revolution in your respective field.
Published by Khanna Publishing House, this Beginner's Guide for Data Analysis using R Programming is your first step into unlocking the power of data analysis and visualization. Dive into the world of R programming today!
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Authors: Jeeva Jose
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Publisher: KHANNA PUBLISHING HOUSE
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Page Count: 368
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en