用数据击中靶心不只是数学和工程学It Takes More than Math and Engineering to Hit the Bullseye with Data |
|
课程网址: | http://videolectures.net/kdd2017_desai_math_engineering_data/ |
主讲教师: | Paritosh Desai |
开课单位: | 塔吉特公司 |
开课时间: | 2017-10-09 |
课程语种: | 英语 |
中文简介: | 在像Target这样的财富50强零售商这样的大型复杂企业中采用算法决策所需要的不仅是干净,可靠的数据和强大的数据挖掘功能。然而,数据从业人员常常从高级数学和幻想算法入手,而不是与业务合作伙伴携手确定和理解最大的业务问题。 (然后,团队应该继续研究如何将算法应用于这些问题。)大型组织中数据科学家的另一个关键步骤:确保其商业伙伴,商人,商人和供应链专家对高级模型以及适当的分析支持工具。要获得广泛的认同和热情,还需要为业务合作伙伴提供用户友好的界面,该界面具有可选项和灵活性,可以将情报应用于现代零售商面临的许多不同问题,从个性化到供应链转型,再到分类和定价决策。这次演讲将探讨有效的实践和流程,以帮助数据科学家在大型,复杂的组织中取得成功,例如拥有1800家商店的零售商,跨多个渠道的大型营销活动以及快速发展的在线业务。 p> |
课程简介: | Adopting algorithmic decision-making in a large and complex enterprise such as a Fortune 50 retailer like Target takes much more than clean, reliable data and great data mining capabilities. Yet data practitioners too often start with advanced math and fancy algorithms, rather than working hand-in-hand with business partners to identify and understand the biggest business problems. (Then teams should move onto how algorithms can be applied to those problems.) Another key step for data scientists at large organizations: ensuring that their business partners -- the merchants, marketers and supply chain experts -- have a base-line understanding of advanced models as well as the proper analytical support tools. Obtaining widespread buy-in and enthusiasm also requires providing a user-friendly interface for business partners with optionality and flexibility that allows the intelligence to be applied to the many varied issues facing a modern retailer, from personalization to supply chain transformation to decisions on assortment and pricing. This talk will explore effective practices and processes -- the do's and don'ts -- for data scientists to succeed in large, complex organizations like a retailer with 1,800+ stores, major marketing campaigns across multiple channels and a fast growing online business. |
关 键 词: | 数据挖掘; 高级模型; 在线业务 |
课程来源: | 视频讲座网 |
数据采集: | 2020-12-06:cjy |
最后编审: | 2020-12-06:cjy |
阅读次数: | 15 |