Big Data and Social Science: Data Science Methods and Tools for Research and Practice | Agenda Bookshop Skip to content
Online orders placed from 19/12 onward will not arrive in time for Christmas.
Online orders placed from 19/12 onward will not arrive in time for Christmas.
Age Group_Uncategorized
Age Group_Uncategorized
automatic-update
B01=Frauke Kreuter
B01=Ian Foster
B01=Julia Lane
B01=Rayid Ghani
B01=Ron S. Jarmin
Category1=Non-Fiction
Category=JMB
Category=PBT
Category=TJFM
COP=United Kingdom
Delivery_Pre-order
Language_English
PA=Temporarily unavailable
Price_€100 and above
PS=Active
softlaunch

Big Data and Social Science: Data Science Methods and Tools for Research and Practice

English

Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations.

Features:

  • Takes an accessible, hands-on approach to handling new types of data in the social sciences
  • Presents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposes
  • Illustrates social science and data science principles through real-world problems
  • Links computer science concepts to practical social science research
  • Promotes good scientific practice
  • Provides freely available workbooks with data, code, and practical programming exercises, through Binder and GitHub

New to the Second Edition:

  • Increased use of examples from different areas of social sciences
  • New chapter on dealing with Bias and Fairness in Machine Learning models
  • Expanded chapters focusing on Machine Learning and Text Analysis
  • Revamped hands-on Jupyter notebooks to reinforce concepts covered in each chapter

This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner.

See more
Current price €137.07
Original price €148.99
Save 8%
Age Group_Uncategorizedautomatic-updateB01=Frauke KreuterB01=Ian FosterB01=Julia LaneB01=Rayid GhaniB01=Ron S. JarminCategory1=Non-FictionCategory=JMBCategory=PBTCategory=TJFMCOP=United KingdomDelivery_Pre-orderLanguage_EnglishPA=Temporarily unavailablePrice_€100 and abovePS=Activesoftlaunch

Will deliver when available.

Product Details
  • Weight: 940g
  • Dimensions: 191 x 235mm
  • Publication Date: 18 Nov 2020
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: United Kingdom
  • Language: English
  • ISBN13: 9780367341879

About

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 Bureaus 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.

Customer Reviews

Be the first to write a review
0%
(0)
0%
(0)
0%
(0)
0%
(0)
0%
(0)
We use cookies to ensure that we give you the best experience on our website. If you continue we'll assume that you are understand this. Learn more
Accept