Practical Statistics For Data Scientists

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Practical Statistics For Data Scientists

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Author : Peter Bruce
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-05-10

Practical Statistics For Data Scientists written by Peter Bruce and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-10 with Computers categories.

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

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Author : Peter Bruce
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-04-10

Practical Statistics For Data Scientists written by Peter Bruce and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-10 with Computers categories.

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

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Author : Peter C. Bruce
language : en
Publisher:
Release Date : 2017

Practical Statistics For Data Scientists written by Peter C. Bruce and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Big data categories.

"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"--Provided by publisher.

Practical Statistics For Data Scientists 2nd Edition

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Author : Peter Bruce
language : en
Publisher:
Release Date : 2020

Practical Statistics For Data Scientists 2nd Edition written by Peter Bruce and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.

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.

Statistics

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Author : John Slavio
language : en
Publisher:
Release Date : 2019-07-24

Statistics written by John Slavio and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-24 with Mathematics categories.

This book is a great reference for you to get started with statistics.

Practical Statistics For Engineers And Scientists

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Author : Nicholas P. Cheremisinoff
language : en
Publisher: CRC Press
Release Date : 1987-11-26

Practical Statistics For Engineers And Scientists written by Nicholas P. Cheremisinoff and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987-11-26 with Mathematics categories.

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.

Statistics For Data Science

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Author : Anshuman Mishra
language : en
Publisher: Independently Published
Release Date : 2025-08-08

Statistics For Data Science written by Anshuman Mishra and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-08 with Computers categories.

Unlike conventional statistics books, this book approaches statistical learning with a pragmatic, industry-relevant lens. Data is no longer confined to labs or theoretical research-it's now at the heart of product design, business decisions, artificial intelligence, and public policy. The author's goal is to empower students to not only understand statistics but also to communicate results, make data-driven decisions, and build predictive models confidently. Each topic, from central tendency to regression analysis, is backed with realistic datasets, contextual narratives, and practical scenarios. For example, while discussing logistic regression, students are guided through applications like predicting customer churn or disease detection using real-world healthcare datasets. This relevance helps learners grasp not only the what and how, but also the why behind every statistical method. Why Statistics Matters in Data Science Statistics is the backbone of data science. Without a good understanding of statistical methods, it's impossible to validate assumptions, interpret models, assess data quality, or make reliable predictions. In this book, the importance of statistical literacy is emphasized right from the start. The reader is encouraged to think critically, explore data patterns, and assess uncertainty-key attributes for any aspiring data professional. The book also walks students through the evolution of data science, highlighting how statistical concepts have powered machine learning, AI, and deep learning models. Even black-box algorithms require a strong grasp of statistical tools for proper tuning, validation, and error analysis. Multidisciplinary Relevance The book is intentionally designed to be multidisciplinary, making it a suitable companion for learners from commerce, management, life sciences, social sciences, and computer science. Whether the student is analyzing survey responses, clinical trials, market data, or customer behavior, the statistical techniques taught in this book remain universally applicable. To facilitate interdisciplinary learning, each chapter includes case studies from multiple domains. For example: Business Analytics: Sales forecasting, A/B testing, customer segmentation Healthcare: Survival analysis, clinical trial interpretation Environment and Agriculture: Climate data modeling, yield prediction Social Science: Survey analysis, behavioral trend studies Curriculum-Aligned and Instructor-Friendly The book has been written keeping in mind the curriculum requirements of major undergraduate programs across universities. It's particularly aligned with CBCS (Choice Based Credit System) and AICTE-approved syllabi. Each unit is mapped to specific learning outcomes, making it easy for instructors to schedule classes, assign homework, and assess learning. Lecture-Wise Breakdown: Each chapter can be broken down into 4-6 lectures Assignments & Lab Work: Ready-to-use coding exercises for Python and R MCQs and Quizzes: Objective-type questions for exam preparation Mini Projects: End-of-semester projects with evaluation rubrics

Practical Statistics For The Analytical Scientist

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Author : Peter Bedson
language : en
Publisher: Royal Society of Chemistry
Release Date : 2021-04-08

Practical Statistics For The Analytical Scientist written by Peter Bedson and has been published by Royal Society of Chemistry this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-08 with Mathematics categories.

Analytical chemists must use a range of statistical tools in their treatment of experimental data to obtain reliable results. Practical Statistics for the Analytical Scientist is a manual designed to help them negotiate the daunting specialist terminology and symbols. Prepared in conjunction with the Department of Trade and Industry's Valid Analytical Measurement (VAM) programme, this volume covers the basic statistics needed in the laboratory. It describes the statistical procedures that are most likely to be required including summary and descriptive statistics, calibration, outlier testing, analysis of variance and basic quality control procedures. To improve understanding, many examples provide the user with material for consolidation and practice. The fully worked answers are given both to check the correct application of the procedures and to provide a template for future problems. Practical Statistics for the Analytical Scientist will be welcomed by practising analytical chemists as an important reference for day to day statistics in analytical chemistry.

The Teaching Of Practical Statistics

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Author : Clive W. Anderson
language : en
Publisher:
Release Date : 1987-12-14

The Teaching Of Practical Statistics written by Clive W. Anderson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987-12-14 with Mathematics categories.

This book is intended to enable teachers of statistics to relate their courses to what practising statisticians do in real life, and to give their students the skills needed to apply their theoretical knowledge to real problems.

Practical Statistics For Environmental And Biological Scientists

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Author : John Townend
language : en
Publisher: John Wiley & Sons
Release Date : 2013-04-30

Practical Statistics For Environmental And Biological Scientists written by John Townend and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-30 with Science categories.

All students and researchers in environmental and biological sciences require statistical methods at some stage of their work. Many have a preconception that statistics are difficult and unpleasant and find that the textbooks available are difficult to understand. Practical Statistics for Environmental and Biological Scientists provides a concise, user-friendly, non-technical introduction to statistics. The book covers planning and designing an experiment, how to analyse and present data, and the limitations and assumptions of each statistical method. The text does not refer to a specific computer package but descriptions of how to carry out the tests and interpret the results are based on the approaches used by most of the commonly used packages, e.g. Excel, MINITAB and SPSS. Formulae are kept to a minimum and relevant examples are included throughout the text.