Big Data and Social Science: Data Science Methods and Tools for Research and Practice
暫譯: 大數據與社會科學：研究與實踐的數據科學方法與工具

《大數據與社會科學：研究與實踐的數據科學方法與工具（第二版）》展示了如何將數據科學應用於現實世界的問題，涵蓋數據密集型社會科學或政策項目的所有階段。社會科學、統計學和計算機科學的知名領導者以及數據科學領域的專家提供了獨特的視角，說明如何應用現代社會科學研究原則及當前的分析和計算工具。該書教導您如何識別和收集適當的數據，將數據科學方法和工具應用於數據，並識別和應對數據錯誤、偏見和限制。

特點

- 採取可接觸的、實踐導向的方法來處理社會科學中新類型的數據
- 以非威脅性的方式向社會科學家和數據科學家介紹關鍵的數據科學工具，同時保持對研究問題和目的的關注
- 通過現實世界的問題來說明社會科學和數據科學的原則
- 將計算機科學概念與實際的社會科學研究聯繫起來
- 促進良好的科學實踐
- 提供免費的數據和代碼，以及通過 Binder 和 GitHub 的實用編程練習

第二版的新內容

- 增加來自不同社會科學領域的範例使用
- 新增關於處理機器學習模型中的偏見和公平性的一章
- 擴展專注於機器學習和文本分析的章節
- 改進的 Jupyter 筆記本以加強每章所涵蓋的概念

這本經過課堂測試的書填補了研究生和專業級數據科學及社會科學教育中的一個重要空白。它可以用來培訓新一代社會數據科學家，以解決現實世界的問題，並提高應用社會科學家和公共政策從業者的技能和能力。它使您能夠利用大量快速增長的可用數據，以科學和嚴謹的方式解釋經濟和社會活動。

Ian Foster, PhD, is a professor of computer science at the University of Chicago as well as a senior scientist and distinguished fellow at Argonne National Laboratory. His research addresses innovative applications of distributed, parallel, and data-intensive computing technologies to scientific problems in such domains as climate change and biomedicine. Methods and software developed under his leadership underpin many large national and international cyberinfrastructures. He is a fellow of the American Association for the Advancement of Science, the Association for Computing Machinery, and the British Computer Society. He earned a PhD in computer science from Imperial College London.

Rayid Ghani is a professor in the Machine Learning Department (in the School of Computer Science) and the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. His research focuses on developing and using Machine Learning, AI, and Data Science methods for solving high impact social good and public policy problems in a fair and equitable way across criminal justice, education, healthcare, energy, transportation, economic development, workforce development and public safety. He is also the founder and director of the "Data Science for Social Good" summer program for aspiring data scientists to work on data mining, machine learning, big data, and data science projects with social impact. Previously Rayid Ghani was a faculty member at University of Chicago, and prior to that, served as the Chief Scientist for Obama for America (Obama 2012 Campaign).

Ron Jarmin, PhD, is the Deputy Director at the U.S. Census Bureau. He earned a PhD in economics from the University of Oregon and has published in the areas of industrial organization, business dynamics, entrepreneurship, technology and firm performance, urban economics, Big Data, data access and statistical disclosure avoidance. He oversees the Census Bureau's large portfolio of data collection, research and dissemination activities for critical economic and social statistics including the 2020 Decennial Census of Population and Housing.

Frauke Kreuter, PhD, is Professor at the University of Maryland in the Joint Program in Survey Methodology, Professor of Statistics and Methodology at the University of Mannheim and head of the Statistical Methods group at the Institute for Employment Research in Nuremberg, Germany. She is founder of the International Program in Survey and Data Science, co-founder of the Coleridge Initiative, fellow of the American Statistical Association (ASA), and recipient of the WSS Cox and the ASA Links Lecture Awards. Her research focuses on data quality, privacy, and the effects of bias in data collection on statistical estimates and algorithmic fairness.

Julia Lane, PhD, is a professor at the NYU Wagner Graduate School of Public Service. She is also an NYU Provostial Fellow for Innovation Analytics. She co-founded the Coleridge Initiative as well as UMETRICS and STAR METRICS programs at the National Science Foundation, established a data enclave at NORC/University of Chicago, and co-founded the Longitudinal Employer-Household Dynamics Program at the U.S. Census Bureau and the Linked Employer Employee Database at Statistics New Zealand. She is the author/editor of 10 books and the author of more than 70 articles in leading journals, including Nature and Science. She is an elected fellow of the American Association for the Advancement of Science and a fellow of the American Statistical Association.

伊恩·福斯特 (Ian Foster), PhD 是芝加哥大學的計算機科學教授，同時也是阿貢國家實驗室的高級科學家和傑出研究員。他的研究針對分散式、平行及數據密集型計算技術在氣候變遷和生物醫學等科學問題上的創新應用。在他的領導下開發的方法和軟體支撐了許多大型國家和國際的網路基礎設施。他是美國科學促進會、計算機協會和英國計算機學會的會士。他在倫敦帝國學院獲得計算機科學博士學位。

雷伊德·甘尼 (Rayid Ghani) 是卡內基梅隆大學機器學習系（計算機科學學院）及海因茨資訊系統與公共政策學院的教授。他的研究專注於開發和使用機器學習、人工智慧和數據科學方法，以公平和公正的方式解決刑事司法、教育、醫療保健、能源、交通、經濟發展、勞動力發展和公共安全等領域的高影響社會公益和公共政策問題。他也是「社會公益數據科學」夏季計畫的創始人和主任，該計畫旨在讓有志於成為數據科學家的學生參與數據挖掘、機器學習、大數據和具有社會影響的數據科學專案。雷伊德·甘尼曾是芝加哥大學的教職員，並在此之前擔任奧巴馬2012年競選活動的首席科學家。

羅恩·賈敏 (Ron Jarmin), PhD 是美國人口普查局的副局長。他在俄勒岡大學獲得經濟學博士學位，並在產業組織、商業動態、創業、技術與公司績效、城市經濟學、大數據、數據存取和統計披露避免等領域發表過論文。他負責人口普查局在關鍵經濟和社會統計方面的大量數據收集、研究和發佈活動，包括2020年十年一次的人口與住房普查。

弗勞克·克魯特 (Frauke Kreuter), PhD 是馬里蘭大學調查方法聯合計畫的教授，並且是曼海姆大學的統計學與方法學教授，以及德國紐倫堡就業研究所統計方法小組的負責人。她是國際調查與數據科學計畫的創始人，科勒里奇倡議的共同創始人，美國統計協會（ASA）的會士，以及WSS Cox和ASA Links演講獎的獲得者。她的研究專注於數據質量、隱私以及數據收集中的偏見對統計估計和算法公平性的影響。

朱莉亞·萊恩 (Julia Lane), PhD 是紐約大學瓦根公共服務研究生院的教授。她同時也是紐約大學創新分析的教務長研究員。她共同創立了科勒里奇倡議以及國家科學基金會的UMETRICS和STAR METRICS計畫，並在NORC/芝加哥大學建立了一個數據保護區，還共同創立了美國人口普查局的縱向雇主-家庭動態計畫和新西蘭統計局的連結雇主-員工數據庫。她是10本書的作者/編輯，並在《Nature》和《Science》等頂尖期刊上發表了70多篇文章。她是美國科學促進會的當選會士，也是美國統計協會的會士。