A half day Tutorial on Data Science and Computational Social Science

29 Nov 2016

Tutorial Schedule

  • 9:00-10:30 Session 1: Introduction to Data Science
  • 10:30-10:50 Tea break
  • 10:50-12:20 Session 2: Introduction to Computational Social Science
  • Introduction

    Data science is an interdisciplinary field about theories, techniques, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured. It is highly related to fundamental fields such as statistics and applied fields such as data mining, pattern recognition, and knowledge discovery in databases, as well as big data engineering/analytics. It seems that data science simply old wine in a new bottle, whereas the fact that a number of new techniques and tools have been invented to resolve issues in analytics and engineering provides a contradiction.

    In this short tutorial, I will give an overview of data science and a number of most relevant fields, including big data and deep learning. The overview comprises the definitions, the causes, and the differences of data science and between relevant fields. Following that, I will base on several case studies to share the audience with my first-hand industrial collaboration experiences on data analytics research. The first case is to help an online game company to predict the lifetime of online games, while the second case is to predict whether a phone call from an unknown number is malicious or not. I will talk how the collaboration started and various technical and non-technical challenges we encountered in the collaboration.

    The second focus of this tutorial is an overview of computational social science, which is an instrument-enabled discipline as it is enabled by big data technologies, just like microbiology enabled by microscope. Computational social science is a young discipline which was formally defined in early 2000. It refers to computational approaches to the social sciences, where empirical research, especially through big data, by analyzing the digital footprint left behind through social online/offline activities is now much empowered by the advances of computing devices (such as mobile phones, wearable devices) and data analytic capabilities (such as computer vision and machine learning). As computational social sciences concerns about the understanding of all types of social phenomena, it's actually quite related to many other fields, such as computer-human interaction, social computing, and even public health. Thus, it is hoped that the audience will relate computational social science to their own researches in some way and even participate in the advances of this interesting, potential, and inter-disciplinary-by-nature field.

    Workshop host

    Dr. Sheng-Wei Chen

    Dr. Sheng-Wei Chen (also known as Kuan-Ta Chen) is a Research Fellow at the Institute of Information Science and the Research Center for Information Technology Innovation (joint appointment) of Academia Sinica. He was an Assistant Research Fellow from 2006 to 2011 and an Associate Research Fellow from 2011 to 2015 at the Institute of Information Science, Academia Sinica. He received his Ph.D. in Electrical Engineering from National Taiwan University in 2006, and his B.S. and M.S. in Computer Science from National Tsing Hua University in 1998 and 2000, respectively. Prior to taking his academic path, he was active as a programmer specialized in Windows system programming, a technical writer, and a freeware/shareware developer.

    His research interests span various areas in multimedia and social computing, with emphases on quality of experience (QoE), multimedia systems, crowdsourcing, and computational social science. He received the Best Paper Award in IWSEC 2008 and K. T. Li Distinguished Young Scholar Award from ACM Taipei/Taiwan Chapter in 2009. He also received the Outstanding Young Electrical Engineer Award from The Chinese Institute of Electrical Engineering in 2010, the Young Scholar's Creativity Award from Foundation for the Advancement of Outstanding Scholarship in 2013, and IEEE ComSoc MMTC Best Journal Paper Award in 2014. He was an Associate Editor of IEEE Transactions on Multimedia (IEEE TMM) during 2011 to 2014 and has been an Associate Editor of ACM Transactions on Multimedia Computing, Communications, and Applications (ACM TOMM) since 2015. He organizes Taiwan Data Science Conference from 2014 to 2016 and was elected as the Chairman of Taiwan Data Science Foundation in 2016. He is a Senior Member of ACM and a Senior Member of IEEE.