Practical Statistics For Data Scientists

Practical Statistics for Data Scientists PDF
Author: Peter Bruce
Publisher: "O'Reilly Media, Inc."
ISBN: 1491952938
Size: 77.69 MB
Format: PDF
Category : Computers
Languages : en
Pages : 318
View: 6911

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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

Practical Statistics For Data Scientists

Practical Statistics for Data Scientists PDF
Author: Peter Bruce
Publisher: O'Reilly Media
ISBN: 1492072915
Size: 42.20 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 368
View: 4711

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Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data

Practical Statistics For Data Scientists 2nd Edition

Practical Statistics for Data Scientists  2nd Edition PDF
Author: Peter Bruce
Publisher:
ISBN:
Size: 43.73 MB
Format: PDF, ePub, Docs
Category :
Languages : en
Pages : 93
View: 2255

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Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-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 scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning.

Practical Statistics For Geographers And Earth Scientists

Practical Statistics for Geographers and Earth Scientists PDF
Author: Nigel Walford
Publisher: John Wiley & Sons
ISBN: 1119957028
Size: 42.97 MB
Format: PDF, ePub, Mobi
Category : Science
Languages : en
Pages : 440
View: 1203

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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

Practical Statistics For Engineers And Scientists

Practical Statistics for Engineers and Scientists PDF
Author: Nicholas P. Cheremisinoff
Publisher: CRC Press
ISBN: 1000159930
Size: 60.63 MB
Format: PDF, ePub, Docs
Category : Mathematics
Languages : en
Pages : 224
View: 4129

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This book provides direction in constructing regression routines that can be used with worksheet software on personal computers. The book lists useful references for those readers who desire more in-depth understanding of the mathematical bases, and is helpful for science and engineering students.

Practical Statistics For The Analytical Scientist

Practical Statistics for the Analytical Scientist PDF
Author: S. L. R. Ellison
Publisher: Royal Society of Chemistry
ISBN: 0854041311
Size: 64.77 MB
Format: PDF, Docs
Category : Science
Languages : en
Pages : 268
View: 1945

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

Practical Statistics For Environmental And Biological Scientists

Practical Statistics for Environmental and Biological Scientists PDF
Author: John Townend
Publisher: John Wiley & Sons
ISBN: 1118687418
Size: 39.15 MB
Format: PDF, Mobi
Category : Science
Languages : en
Pages : 272
View: 6609

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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.

Practical Statistics For Pharmaceutical Analysis

Practical Statistics for Pharmaceutical Analysis PDF
Author: James E. De Muth
Publisher: Springer
ISBN: 9783030339883
Size: 15.98 MB
Format: PDF, ePub, Mobi
Category : Medical
Languages : en
Pages : 245
View: 7484

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This is an introductory statistics book designed to provide scientists with practical information needed to apply the most common statistical tests to laboratory research data. The book is designed to be practical and applicable, so only minimal information is devoted to theory or equations. Emphasis is placed on the underlying principles for effective data analysis and survey the statistical tests. It is of special value for scientists who have access to Minitab software. Examples are provides for all the statistical tests and explanation of the interpretation of these results presented with Minitab (similar to results for any common software package). The book is specifically designed to contribute to the AAPS series on advances in the pharmaceutical sciences. It benefits professional scientists or graduate students who have not had a formal statistics class, who had bad experiences in such classes, or who just fear/don’t understand statistics. Chapter 1 focuses on terminology and essential elements of statistical testing. Statistics is often complicated by synonyms and this chapter established the terms used in the book and how rudiments interact to create statistical tests. Chapter 2 discussed descriptive statistics that are used to organize and summarize sample results. Chapter 3 discussed basic assumptions of probability, characteristics of a normal distribution, alternative approaches for non-normal distributions and introduces the topic of making inferences about a larger population based on a small sample from that population. Chapter 4 discussed hypothesis testing where computer output is interpreted and decisions are made regarding statistical significance. This chapter also deasl with the determination of appropriate sample sizes. The next three chapters focus on tests that make decisions about a population base on a small subset of information. Chapter 5 looks at statistical tests that evaluate where a significant difference exists. In Chapter 6 the tests try to determine the extent and importance of relationships. In contrast to fifth chapter, Chapter 7 presents tests that evaluate the equivalence, not the difference between levels being tested. The last chapter deals with potential outlier or aberrant values and how to statistically determine if they should be removed from the sample data. Each statistical test presented includes an example problem with the resultant software output and how to interpret the results. Minimal time is spent on the mathematical calculations or theory. For those interested in the associated equations, supplemental figures are presented for each test with respective formulas. In addition, Appendix D presents the equations and proof for every output result for the various examples. Examples and results from the appropriate statistical results are displayed using Minitab 18Ò. In addition to the results, the required steps to analyze data using Minitab are presented with the examples for those having access to this software. Numerous other software packages are available, including based data analysis with Excel.

Data Science With Java

Data Science with Java PDF
Author: Michael R. Brzustowicz, PhD
Publisher: "O'Reilly Media, Inc."
ISBN: 1491934069
Size: 71.45 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 236
View: 877

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Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today’s data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java. You’ll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you’ll find code examples you can use in your applications. Examine methods for obtaining, cleaning, and arranging data into its purest form Understand the matrix structure that your data should take Learn basic concepts for testing the origin and validity of data Transform your data into stable and usable numerical values Understand supervised and unsupervised learning algorithms, and methods for evaluating their success Get up and running with MapReduce, using customized components suitable for data science algorithms

R For Political Data Science

R for Political Data Science PDF
Author: Francisco Urdinez
Publisher: CRC Press
ISBN: 9780367818890
Size: 52.55 MB
Format: PDF, ePub
Category : Political statistics
Languages : en
Pages : 440
View: 5401

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R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists, thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first, on the use of R, is for those users who are learning R or are migrating from another software. The second section, on econometric models, covers OLS, binary and survival models, panel data, and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation, fuzzy merge of large datasets, web mining, quantitative text analysis, network analysis, mapping, spatial cluster analysis, and principal component analysis. Key features: Each chapter has the most up-to-date and simple option available for each task, assuming minimal prerequisites and no previous experience in R Makes extensive use of the Tidyverse, the group of packages that has revolutionized the use of R Provides a step-by-step guide that you can replicate using your own data Includes exercises in every chapter for course use or self-study Focuses on practical-based approaches to statistical inference rather than mathematical formulae Supplemented by an R package, including all data As the title suggests, this book is highly applied in nature, and is designed as a toolbox for the reader. It can be used in methods and data science courses, at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD, political consultants, or a public official, all of whom need to transform their datasets into substantive and easily interpretable conclusions.