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Ethical Practice of Statistics and Data Science

Author: Rochelle Tractenberg

Publisher: Ethics International Press

Published: 2022-10-25

Total Pages: 685

ISBN-13: 1804410772

Ethical Practice of Statistics and Data Science is intended to prepare people to fully assume their responsibilities to practice statistics and data science ethically. Aimed at early career professionals, practitioners, and mentors or supervisors of practitioners, the book supports the ethical practice of statistics and data science, with an emphasis on how to earn the designation of, and recognize, “the ethical practitioner”. The book features 47 case studies, each mapped to the Data Science Ethics Checklist (DSEC); Data Ethics Framework (DEFW); the American Statistical Association (ASA) Ethical Guidelines for Statistical Practice; and the Association of Computing Machinery (ACM) Code of Ethics. It is necessary reading for students enrolled in any data intensive program, including undergraduate or graduate degrees in (bio-)statistics, business/analytics, or data science. Managers, leaders, supervisors, and mentors who lead data-intensive teams in government, industry, or academia would also benefit greatly from this book. This is a companion volume to Ethical Reasoning For A Data-Centered World, also published by Ethics International Press (2022). These are the first and only books to be based on, and to provide guidance to, the ASA and ACM Ethical Guidelines/Code of Ethics.

Ethical Practice of Statistics and Data Science

Author: Rochelle Tractenberg

Publisher:

Published: 2023

Total Pages: 0

ISBN-13: 9781804412671

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Ethical Practice of Statistics and Data Science is intended to prepare people to fully assume their responsibilities to practice statistics and data science ethically. Aimed at early career professionals, practitioners, and mentors or supervisors of practitioners, the book supports the ethical practice of statistics and data science, with an emphasis on how to earn the designation of, and recognize, the ethical practitioner . The book features 47 case studies, each mapped to the Data Science Ethics Checklist (DSEC); Data Ethics Framework (DEFW); the American Statistical Association (ASA) Ethical Guidelines for Statistical Practice; and the Association of Computing Machinery (ACM) Code of Ethics. It is necessary reading for students enrolled in any data intensive program, including undergraduate or graduate degrees in (bio-)statistics, business/analytics, or data science. Managers, leaders, supervisors, and mentors who lead data-intensive teams in government, industry, or academia would also benefit greatly from this book. This is a companion volume to Ethical Reasoning For A Data-Centered World, also published by Ethics International Press (2022). These are the first and only books to be based on, and to provide guidance to, the ASA and ACM Ethical Guidelines/Code of Ethics.

Ethical Reasoning for a Data-Centered World

Author: Rochelle Tractenberg

Publisher: Ethics International Press

Published: 2022-10-25

Total Pages: 411

ISBN-13: 1804410799

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The American Statistical Association (ASA) and the Association of Computing Machinery (ACM) have longstanding ethical practice standards that are explicitly intended to be utilized by all who use statistical practices or computing, or both. Since statistics and computing are critical in any data-centered activity, these practice standards are essential to instruction in the uses of statistical practices or computing across disciplines. Ethical Reasoning For A Data-Centered World is aimed at any undergraduate or graduate students utilizing data. Whether the career goal is research, teaching, business, government, or a combination, this book presents a method for understanding and prioritizing ethical statistics, computing, and data science – featuring the ASA and ACM practice standards. To facilitate engagement, integration with prior learning, and authenticity, the material is organized around seven tasks: Planning/Designing; Data collection; Analysis; Interpretation; Reporting; Documenting; and Engaging in team work. This book is a companion volume to Ethical Practice of Statistics and Data Science, also published by Ethics International Press (2022). These are the first and only books to be based on, and to provide guidance to, the American Statistical Association (ASA) and Association of Computing Machinery (ACM) ethical guideline documents.

Ethical Reasoning for a Data-Centered World

Author: Rochelle Tractenberg

Publisher:

Published: 2023

Total Pages: 0

ISBN-13: 9781804412688

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The American Statistical Association (ASA) and the Association of Computing Machinery (ACM) have longstanding ethical practice standards that are explicitly intended to be utilized by all who use statistical practices or computing, or both. Since statistics and computing are critical in any data-centered activity, these practice standards are essential to instruction in the uses of statistical practices or computing across disciplines. Ethical Reasoning For A Data-Centered World is aimed at any undergraduate or graduate students utilizing data. Whether the career goal is research, teaching, business, government, or a combination, this book presents a method for understanding and prioritizing ethical statistics, computing, and data science featuring the ASA and ACM practice standards. To facilitate engagement, integration with prior learning, and authenticity, the material is organized around seven tasks: Planning/Designing; Data collection; Analysis; Interpretation; Reporting; Documenting; and Engaging in team work. This book is a companion volume to Ethical Practice of Statistics and Data Science, also published by Ethics International Press (2022). These are the first and only books to be based on, and to provide guidance to, the American Statistical Association (ASA) and Association of Computing Machinery (ACM) ethical guideline documents.

Ethics and Data Science

Author: Mike Loukides

Publisher: O'Reilly Media

Published: 2018-07-25

Total Pages: 40

ISBN-13: 1492078220

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As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C’s) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.

Applied Data Science

Author: Douglas G. Woolford

Publisher: Springer Nature

Published: 2023-05-09

Total Pages: 195

ISBN-13: 303129937X

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The use of data to guide action is growing. Even the public uses data to guide everyday decisions! How do we develop data acumen across a broad range of fields and varying levels of expertise? How do we foster the development of effective data translators? This book explores these questions, presenting an interdisciplinary collection of edited contributions across fields such as education, health sciences, natural sciences, politics, economics, business and management studies, social sciences, and humanities. Authors illustrate how to use data within a discipline, including visualization and analysis, translating and communicating results, and pedagogical considerations. This book is of interest to scholars and anyone looking to understand the use of data science across disciplines. It is ideal in a course for non-data science majors exploring how data translation occurs in various contexts and for professionals looking to engage in roles requiring data translation.

Modern Data Science with R

Author: Benjamin S. Baumer

Publisher: CRC Press

Published: 2021-03-31

Total Pages: 830

ISBN-13: 0429575394

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From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.

Education for the Professions in Times of Change

Author: Linda Clarke

Publisher: MDPI

Published: 2020-12-02

Total Pages: 186

ISBN-13: 3039365150

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The eminent Harvard educationalist Howard Garner writes a preface to the Place Model within his Good Project Blog which provides a preface to this timely book. Professional is a slippery term, open to willful abuse, misuse and misunderstanding – as evidenced by the ways in which this chameleon term can be used as both a compliment and an insult. In this book academics from a range of professional fields deconstruct ‘professional’ and reimagine professionals in an age of rapid change where professionals are both increasingly in demand and frequently under threat. Several deploy the lens of Clarke’s Place Model to examine professions including teaching, midwifery, social work, journalism, and optometry. Some papers are empirical and some are based around using the Place Model as a thought experiment. All turn a critical eye on professionals and all find them to be, like all humans, neither devils nor divines (Maya Angelou), but at their best a combination of two indispensable characteristics, trustworthiness and expertise.

Responsible Data Science

Author: Peter C. Bruce

Publisher: John Wiley & Sons

Published: 2021-04-13

Total Pages: 304

ISBN-13: 1119741777

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Explore the most serious prevalent ethical issues in data science with this insightful new resource The increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of “Black box” algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair. Responsible Data Science delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to: Improve model transparency, even for black box models Diagnose bias and unfairness within models using multiple metrics Audit projects to ensure fairness and minimize the possibility of unintended harm Perfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians.

Modern Statistics with R

Author: Måns Thulin

Publisher: CRC Press

Published: 2024-08-20

Total Pages: 0

ISBN-13: 9781032512440

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The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit. It teaches you: Data wrangling - importing, formatting, reshaping, merging, and filtering data in R. Exploratory data analysis - using visualisations and multivariate techniques to explore datasets. Statistical inference - modern methods for testing hypotheses and computing confidence intervals. Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting. Simulation - using simulation techniques for sample size computations and evaluations of statistical methods. Ethics in statistics - ethical issues and good statistical practice. R programming - writing code that is fast, readable, and (hopefully!) free from bugs. No prior programming experience is necessary. Clear explanations and examples are provided to accommodate readers at all levels of familiarity with statistical principles and coding practices. A basic understanding of probability theory can enhance comprehension of certain concepts discussed within this book. In addition to plenty of examples, the book includes more than 200 exercises, with fully worked solutions available at: www.modernstatisticswithr.com.