Practical Statistics For Data Scientists 2nd Edition
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Practical Statistics For Data Scientists
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Author : Peter Bruce
language : en
Publisher: O'Reilly Media
Release Date : 2020-04-10
Practical Statistics For Data Scientists written by Peter Bruce and has been published by O'Reilly Media 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 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.
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
Advancing Into Analytics
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Author : George Mount
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-01-22
Advancing Into Analytics written by George Mount 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 2021-01-22 with Computers categories.
Data analytics may seem daunting, but if you're familiar with Excel, you have a head start that can help you make the leap into analytics. Advancing into Analytics will lower your learning curve. Author George Mount, founder and CEO of Stringfest Analytics, clearly and gently guides intermediate Excel users to a solid understanding of analytics and the data stack. This book demonstrates key statistical concepts from spreadsheets and pivots your existing knowledge about data manipulation into R and Python programming. With this practical book at your side, you'll learn how to: Explore a dataset for potential research questions to check assumptions and to build hypotheses Make compelling business recommendations using inferential statistics Load, view, and write datasets using R and Python Perform common data wrangling tasks such as sorting, filtering, and aggregating using R and Python Navigate and execute code in Jupyter notebooks Identify, install, and implement the most useful open source packages for your needs And more.
Practice R
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Author : Edgar J. Treischl
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2023-05-08
Practice R written by Edgar J. Treischl and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-08 with Social Science categories.
Many students learn to analyze data using commercial packages, even though there is an open-source software with cutting-edge possibilities: R, a programming language with countless cool features for applied empirical research. Practice R introduces R to social science students, inspiring them to consider R as an excellent choice. In a non-technical pragmatic way, this book covers all typical steps of applied empirical research. Learn how to prepare, analyze, and visualize data in R. Discover how to collect data, generate reports, or automate error-prone tasks. The book is accompanied by an R package. This provides further learning materials that include interactive tutorials, challenging you with typical problems of applied research. This way, you can immediately practice the knowledge you have learned. The package also includes the source code of each chapter and templates that help to create reports. Practice R has social science students in mind, nonetheless a broader audience may use Practice R to become a proficient R user.
Football Analytics With Python R
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Author : Eric A. Eager
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-08-15
Football Analytics With Python R written by Eric A. Eager 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 2023-08-15 with Computers categories.
Baseball is not the only sport to use "moneyball." American football fans, teams, and gamblers are increasingly using data to gain an edge against the competition. Professional and college teams use data to help select players and identify team needs. Fans use data to guide fantasy team picks and strategies. Sports bettors and fantasy football players are using data to help inform decision making. This concise book provides a clear introduction to using statistical models to analyze football data. Whether your goal is to produce a winning team, dominate your fantasy football league, qualify for an entry-level football analyst position, or simply learn R and Python using fun example cases, this book is your starting place. You'll learn how to: Apply basic statistical concepts to football datasets Describe football data with quantitative methods Create efficient workflows that offer reproducible results Use data science skills such as web scraping, manipulating data, and plotting data Implement statistical models for football data Link data summaries and model outputs to create reports or presentations using tools such as R Markdown and R Shiny And more
Visualizing History S Fragments
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Author : Ashley R. Sanders
language : en
Publisher: Springer Nature
Release Date : 2024-06-19
Visualizing History S Fragments written by Ashley R. Sanders and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-19 with History categories.
This book combines a methodological guide with an extended case study to show how digital research methods can be used to explore how ethnicity, gender, and kinship shaped early modern Algerian society and politics. However, the approaches presented have applications far beyond this specific study. More broadly, these methods are relevant for those interested in identifying and studying relational data, demographics, politics, discourse, authorial bias, and social networks of both known and unnamed actors. Ashley R. Sanders explores how digital research methods can be used to study archival specters – people who lived, breathed, and made their mark on history, but whose presence in the archives and extant documents remains limited, at best, if not altogether lost. Although digital tools cannot metaphorically resurrect the dead nor fill archival gaps, they can help us excavate the people-shaped outlines of those who might have filled these spaces. The six methodological chapters explain why and how each research method is used, present the visual and quantitative results, and analyze them within the context of the historical case study. In addition, every dataset is available on SpringerLink as Electronic Supplementary Material (ESM), and each chapter is accompanied by one or more video tutorials that demonstrate how to apply each of the techniques described (accessed via the SN More Media App).
97 Things About Ethics Everyone In Data Science Should Know
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Author : Bill Franks
language : en
Publisher: O'Reilly Media
Release Date : 2020-08-06
97 Things About Ethics Everyone In Data Science Should Know written by Bill Franks and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-06 with Computers categories.
Most of the high-profile cases of real or perceived unethical activity in data science arenâ??t matters of bad intent. Rather, they occur because the ethics simply arenâ??t thought through well enough. Being ethical takes constant diligence, and in many situations identifying the right choice can be difficult. In this in-depth book, contributors from top companies in technology, finance, and other industries share experiences and lessons learned from collecting, managing, and analyzing data ethically. Data science professionals, managers, and tech leaders will gain a better understanding of ethics through powerful, real-world best practices. Articles include: Ethics Is Not a Binary Conceptâ??Tim Wilson How to Approach Ethical Transparencyâ??Rado Kotorov Unbiased ≠ Fairâ??Doug Hague Rules and Rationalityâ??Christof Wolf Brenner The Truth About AI Biasâ??Cassie Kozyrkov Cautionary Ethics Talesâ??Sherrill Hayes Fairness in the Age of Algorithmsâ??Anna Jacobson The Ethical Data Storytellerâ??Brent Dykes Introducing Ethicizeâ?¢, the Fully AI-Driven Cloud-Based Ethics Solution!â??Brian Oâ??Neill Be Careful with "Decisions of the Heart"â??Hugh Watson Understanding Passive Versus Proactive Ethicsâ??Bill Schmarzo
Python For Scientific Computing And Artificial Intelligence
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Author : Stephen Lynch
language : en
Publisher: CRC Press
Release Date : 2023-04-28
Python For Scientific Computing And Artificial Intelligence written by Stephen Lynch and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-28 with Computers categories.
Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI). This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling. Features: No prior experience of programming is required Online GitHub repository available with codes for readers to practice Covers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computing Full solutions to exercises are available as Jupyter notebooks on the Web Support Material GitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/ Solutions to All Exercises: Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/Solutions_Section_1.html Section 2: Python for Scientific Computing: https://drstephenlynch.github.io/webpages/Solutions_Section_2.html Section 3: Artificial Intelligence: https://drstephenlynch.github.io/webpages/Solutions_Section_3.html
Practical Statistics For Geographers And Earth Scientists
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Author : Nigel Walford
language : en
Publisher: John Wiley & Sons
Release Date : 2025-03-17
Practical Statistics For Geographers And Earth Scientists written by Nigel Walford 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 2025-03-17 with Science categories.
A practice-oriented and accessible introduction to geographical statistics In the newly revised Second Edition of Practical Statistics for Geographers and Earth Scientists, distinguished researcher Nigel Walford delivers an authoritative and easy-to-follow introduction to the principles and applications of statistical analysis in a geographical context. The book assists students in the development of competence in the statistical procedures necessary to conduct independent investigations, field-work, and related geographical research projects. The book explains statistical techniques relevant to geographical, geospatial, earth, and environmental data. It employs graphics and mathematical notation for maximum clarity. Guidance is provided on how to formulate research questions to ensure that the correct data is collected for the chosen analysis method. This new edition incorporates a new section on exploratory spatial analysis and spatial statistics. It also offers: A thorough introduction to first principles in the statistical analysis of geographical data, including discussions of the quality, content, collection, and acquisition of geographical data In-depth treatments of geographical data exploration, including the taking of statistical measures or quantities, as well as frequency distributions, probability, and hypotheses Comprehensive explorations of testing times, including parametric and nonparametric tests Insightful discussions of relationship investigation, including correlation and regression, as well as the spatial aspects of geographical data Perfect for undergraduates pursuing a degree in geography, Practical Statistics for Geographers and Earth Scientists will also be a valuable tool for students in other earth and environmental sciences.