Data Analysis and Rationality in a Complex World (eBook)

Theodore Chadjipadelis, Berthold Lausen, Angelos Markos, Tae Rim Lee, Angela Montanari, Rebecca Nugent (Herausgeber)

eBook Download: PDF

2021 | 1st ed. 2021
XVII, 349 Seiten
Springer International Publishing (Verlag)
978-3-030-60104-1 (ISBN)

Infos

This volume presents the latest advances in statistics and data science, including theoretical, methodological and computational developments and practical applications related to classification and clustering, data gathering, exploratory and multivariate data analysis, statistical modeling, and knowledge discovery and seeking. It includes contributions on analyzing and interpreting large, complex and aggregated datasets, and highlights numerous applications in economics, finance, computer science, political science and education. It gathers a selection of peer-reviewed contributions presented at the 16th Conference of the International Federation of Classification Societies (IFCS 2019), which was organized by the Greek Society of Data Analysis and held in Thessaloniki, Greece, on August 26-29, 2019.

Theodore Chadjipadelis is a Full Professor of Applied Statistics at the School of Political Sciences at the Aristotle University of Thessaloniki, Greece and former Head of the Department from 2006 to 2009 and from 2013 to 2016. His main research interests are in the field of applied statistics, statistics education, electoral and political behaviour, urban and regional planning, and e-governance. He coordinates the Greek section of the C.C.S. (Comparative Candidates Survey) and the C.S.E.S. (Comparative Study of Electoral Systems) programmes. He has published more than 100 papers in international journals, encyclopedias, conference proceedings and edited books.

Berthold Lausen is a Full Professor of Data Science and Head of the Department of Mathematical Sciences at the University of Essex, Colchester, UK, former president of the International Federation of Classification Societies (IFCS) from 2018 to 2019, former president of the Data Science Society (GfKl) from 2013 to 2019 and founding vice president of the European Association for Data Science (EuADS) from 2015 to 2018. Since 2014 he has lead on the introduction and development of data science education at the University of Essex. His research interests are in the field of artificial intelligence, biostatistics, classification, clinical research, data science and machine learning. He has published more than 100 papers in international journals, conference proceedings and edited books.

Angelos Markos is an Associate Professor of Data Analysis in the Social Sciences at the School of Education at the Democritus University of Thrace, Greece. He is a Board Member of the Greek Society of Data Analysis since 2009. His research interests are in the field of multivariate data analysis, dimension reduction and clustering, particularly correspondence analysis and related methods. He has published more than 50 papers in international journals, encyclopedias, conference proceedings and edited books.

Tae Rim Lee is an Honorary Professor of the Department of Data Science & Statistics, and former Dean of the College of Natural Science at the KNOU in Seoul. She is a biostatistician and her main research interests include tree-based classification model with CART, FACT, NN, kernel discrimination, deep learning for HCC patients, survival tree for OSCC. She was the president of KOSHIS (2009-2011) and KCS (2008-2016), council member of IFCS (2008-now), treasurer (2002-2004), former vice president of KSS (2014-2015), the vice president of IASE under ISI (2011-2013). She was elected as an Executive Board Director of IBS (2015-2021) and Organizing Committee Member of International Prize in Statistical Foundation (2018-now). 

Angela Montanari is a Full Professor of Statistics and Head of the Department of Statistical Sciences at the University of Bologna, Italy, the president of the International Federation of Classification Societies (IFCS) from 2020 to 2021, former president of the Classification Group of the Italian Statistical Society (CLADAG) from 2007 to 2009. Her research interests are in the field of supervised and unsupervised classification, dimension reduction, data science and machine learning. She has published more than 100 papers in international journals, conference proceedings and edited books.

Rebecca Nugent is the Stephen E. and Joyce Fienberg Professor of Statistics & Data Science, the Associate Department Head and Co-Director of Undergraduate Studies for the Statistics & Data Science Department at the Carnegie Mellon University in Pittsburgh. She is the President-Elect of the International Federation of Classification Societies (2020-2021) and the former President of the Classification Society (of North America) from 2012-2014.  She has won several national and university teaching awards including the American Statistical Association Waller Award for Innovation in Statistics Education and serves as one of the co-editors of the Springer Texts in Statistics.  She recently served on the National Academy of Sciences study on Envisioning the Data Science Discipline:  The Undergraduate Perspective and is the co-chair of the 2020 NAS study Improving Defense Acquisition Workforce Capability in Data Use.  She has worked extensively in clustering and classification methodology with an emphasis on high-dimensional, big data problems and record linkage applications.  Her current research focus is the development and deployment of low-barrier data analysis platforms that allow for adaptive instruction and the study of data science as a science.  

Produkt-Details

• Erscheint lt. Verlag: 15.2.2021
• Reihe/Serie: Studies in Classification, Data Analysis, and Knowledge Organization
• Reihe/Serie: Studies in Classification, Data Analysis, and Knowledge Organization
• Zusatzinfo: XVII, 349 p. 65 illus., 37 illus. in color.
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Schlagworte: 62-XX, 62-06, 62-07, 62Hxx, 62H30, 62Pxx, 62Jxx • aggregated data • classification • Clustering • Complex Data • computational methods • Data Analysis • data integrity and data overload • Data Science • desicion making • Exploratory data analysis • Knowledge Discovery • multivariate analysis • Multivariate Data Analysis • statistical modeling
• ISBN-10: 3-030-60104-8 / 3030601048
• ISBN-13: 978-3-030-60104-1 / 9783030601041