題目： Tech Mining Using Python
主講人：Scott W. Cunningham 教授
時(shí)間： 2016年5月11日9:00-11:00
地點(diǎn)：主樓418
主講人介紹：
Scott W. Cunningham，荷蘭代爾夫特理工大學(xué)政策研究及系統(tǒng)工程專業(yè)教授，技術(shù)管理領(lǐng)域著名期刊《Technological Forecasting and Social Change》雜志和《International Journal of Innovation and Technology Management》副主編，英國蘇塞克斯大學(xué)（University of Sussex）科技創(chuàng)新政策博士。曾在代爾夫特理工大學(xué)與哈爾濱工業(yè)大學(xué)的合作辦學(xué)項(xiàng)目任教，出版《Forecasting and Management of Technology》（第2版，Wiley出版社，2011），曾就職于美國電話電報(bào)公司及多家大型數(shù)據(jù)庫公司，從事以數(shù)據(jù)分析支撐決策指定的工作，并擔(dān)任電子產(chǎn)品制造業(yè)顧問，致力于通過決策方法研究、內(nèi)容分析法、博弈論進(jìn)行技術(shù)分析和戰(zhàn)略管理。此外，他還是Portland International Conference on the Management of Engineering & Technology (PICMET)和The International Conference on Innovative Methods for Innovation Management and Policy程序委員會(huì)委員，Technological Forecasting & Social Change, International Journal of Innovation and Technology Management, Scientometrics, PLoS ONE, Engineering and Technology Management等國際期刊的審稿人。
內(nèi)容介紹：
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.
（承辦：實(shí)驗(yàn)室，科研與學(xué)術(shù)交流中心）
