在科罗拉多大学教授预测分析
公司规模
Large Corporate
地区
- America
国家
- United States
产品
- DataRobot
- Alteryx
技术栈
- Python
- R
- SQL
- Excel
实施规模
- Departmental Deployment
影响指标
- Digital Expertise
- Productivity Improvements
技术
- 分析与建模 - 预测分析
- 应用基础设施与中间件 - 数据交换与集成
适用行业
- 教育
适用功能
- 产品研发
用例
- 预测性维护
- 质量预测分析
服务
- 数据科学服务
关于客户
本案例研究中的客户是科罗拉多大学，具体来说是利兹商学院。该大学是位于美国的一所大型教育机构。利兹商学院是大学内的一个部门，为学生提供各种与商业相关的课程。该学院致力于为学生提供在商业世界中取得成功所需的技能和知识。这包括向他们传授商业领域的最新技术和趋势，例如预测分析。该学院认识到预测分析在重塑商业和社会方面的重要性，因此热衷于将其纳入课程。
挑战
预测分析正在重塑商业和社会，引发了关于高校应如何培养毕业生的严肃问题。答案之一可能是向所有商学院学生教授预测分析。实现这一重要愿景需要什么？为什么目前还没有实现？作为一名商业分析教授，Kai Larsen 的目标是教授各种各样的学生：那些立即了解预测分析如何重塑其未来工作（信息管理和营销）的学生，那些早已将不同风格的商业分析融入其领域核心的学生（运营管理和金融），以及那些预测分析目前仅重塑其学科的一小部分的学生（会计）。很明显，所有这些学生都必须至少从概念上理解预测分析，以便做出影响其公司未来的决策，因为机器学习工具将继续提供商业洞察力并推动企业内部和外部的变革。
解决方案
要想成功教授预测分析，Larsen 教授认为必须解决两个复杂问题：特定算法的数据预处理以及算法评估与选择。有数百种甚至数千种机器学习算法可供选择。有些算法是商业限制的，但大多数都是开源的，可以在 R 和 Python 中的特定软件包或专用的前沿软件包（如 Google 的 Tensorflow）中找到。预测分析过去需要理解所有这些算法，并且知道如何在它们之间进行选择；同一种算法很少能同时解决两个不同的问题。随着数据量的增长，评估所有这些算法会给教授预测分析所需的基础设施带来巨大挑战。这两个复杂性共同解释了为什么预测分析一直属于为期一年的分析学硕士课程的范围，并且到目前为止还不在核心商业课程范围内。
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