时间：2019年5月25日——2019年5月30日

地点：商学院A1003

主讲人、报告人简介：

    刘晓钟，美国印第安纳大学信息与计算学院副教授，美国雪城大学博士，阿里巴巴达摩院资深算法专家，曾多次担任JCDL、CIKM、ECIR等国际重要会议主席团成员，JASIS&T、IP&M、Journal of Informetrics、ACM Transactions on Information Systems、Journal of Library and Information Science等期刊同行评审专家。研究领域包括信息检索、自然语言处理、文本挖掘、网络挖掘、人类计算等。现从事自然语言处理-文本挖掘方向的研究。

讲座题目：Big Data Analytics and Mining for Information Science

讲座内容：Data science is a rising discipline that uses data to effectively characterize, interpret or predict complex real-world problems. The importance of data science also lies in its huge potential of changing our current way of doing science and social sciences. Big data, featuring in its high heterogeneity and volume, calls for our new understanding and skills towards data and data operations. Due to these features, new methods are needed to process and analyze large-scale data. The class focuses on analytics of two types of big data: web and text data.

This course introduces the fundamentals and the most-recent efforts of data science and big data analysis by focusing on: theoretical aspects, such as their philosophical grounds and implications, and methodological aspects, such as numerical and textual data processing, basic statistical analysis and machine/deep learning, data retrieval and recommendation, data representation and semantics, big data storage, along with several case studies. In addition, this course will introduce the industry researches in data science and natural language processing.

讲座目标：The course has two goals: to develop students’ conceptual understanding of how data science is revolutionizing classical information management and scientific inquiry, and second, to help students acquire hands-on experience and basic implementation capabilities to grasp data science.

讲座主题：Topics we will cover:

1.Text Mining and Natural Language Processing

2.Information Retrieval and Information Seeking

3.Scholarly Information Analysis and Bibliometrics

4.Data Science in Industry

5.Advanced Research Topics in Information Science by using Big Data

讲座时间安排表

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