题目： Tech Mining Using Python
主讲人：Scott W. Cunningham 教授
时间： 2016年5月11日9:00-11:00
地点：主楼418
主讲人介绍：
    Scott W. Cunningham，荷兰代尔夫特理工大学政策研究及系统工程专业教授，技术管理领域著名期刊《Technological Forecasting and Social Change》杂志和《International Journal of Innovation and Technology Management》副主编，英国苏塞克斯大学（University of Sussex）科技创新政策博士。曾在代尔夫特理工大学与哈尔滨工业大学的合作办学项目任教，出版《Forecasting and Management of Technology》（第2版，Wiley出版社，2011），曾就职于美国电话电报公司及多家大型数据库公司，从事以数据分析支撑决策指定的工作，并担任电子产品制造业顾问，致力于通过决策方法研究、内容分析法、博弈论进行技术分析和战略管理。此外，他还是Portland International Conference on the Management of Engineering & Technology (PICMET)和The International Conference on Innovative Methods for Innovation Management and Policy程序委员会委员，Technological Forecasting & Social Change, International Journal of Innovation and Technology Management, Scientometrics, PLoS ONE, Engineering and Technology Management等国际期刊的审稿人。
内容介绍：
    In this lecture I provide a brief overview over tech mining, which is the process of measuring and instrumenting the innovation process. Tech mining is significant enterprise given the strong impacts of innovation on health, welfare, competitiveness and governance. Modern innovative processes involve understanding and anticipating spill-over effects, and the ability to anticipate and absorb the impacts of participating in an open, often global system of innovation. Open source movements – including open data, open innovation, and open source software – help to motivate a range of new approaches in tech mining. I provide a brief overview over a prominent model of innovation – the chain-linked process – and I describe some of the challenges of measuring innovative progress in the chain-linked model. Measurement involves using both input indicators as well as output indicators. Output indicators include intermediate measures of scientific or technological progress including publications, patents, and new product announcements. There are a variety of public and proprietary sources of information for use in tracking outputs. I describe some of the most prominent which I use in my own research. Tech mining is increasingly understood as a form of data science, with similar processes, techniques and methods. I provide a short overview over these processes and methods. In particular I describe the tools which are available to the tech miner. There are proprietary tools, including VantagePoint, as well as open source tools including the Python language. I discuss the strengths and weaknesses of both sets of tools. The lecture concludes with a demonstration of the range of data mining and transformation tasks available in Python. I discuss the resources available to help reduce the learning curve for Python data mining tasks.

（承办：实验室，科研与学术交流中心）