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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-project-management/
课程评论：没有评论
**课程名称：** 数据科学项目管理全流程 **课程概述：** 本课程旨在帮助学员成功管理数据科学项目。课程以流程为导向，详细讲解了 CRISP-DM（跨行业数据挖掘标准过程）方法论。CRISP-DM 是最广泛使用、最全面的数据科学项目框架。本课程将数据挖掘活动置于项目管理的背景下进行阐述，详细介绍各项活动所需的输入和输出，以实现有效的数据科学项目管理。 遵循项目管理最佳实践，本课程指导您如何与合适的利益相关者合作，设定数据挖掘成功标准，以最终达成业务目标。 虽然机器学习和模型构建（使用 Python 或 R）是数据科学项目中的重要环节，但数据科学项目还包含其他关键活动。数据需要经过准备才能应用机器学习技术，而这一过程涉及大量步骤，旨在使数据集能够满足数据科学项目的业务目标。本课程将进行更广阔的视角审视，识别 CRISP-DM 各项活动如何协同作用，以实现数据科学项目的业务成果。 课程涵盖了数据科学项目执行过程中的监控、报告和用户培训需求。数据科学项目最终需要成功部署数据挖掘成果，并回顾总结经验教训。本课程将上述所有活动进行了排序，并对其目的和详细执行步骤进行了深入阐述，以帮助学员端到端地完成数据科学项目。
This course is to enable learners to successfully manage a data science project. It is process oriented and explains CRISP-DM methodology. CRISP-DM, stands for Cross Industry Standard Process for Data Mining, and it is the most widely used, holistic framework for data science projects. This course takes you through the data mining activities in the context of Project Management. The project explains inputs and outputs of all activities helping effective project management of a data science project. As per the Project Management best practices it guides you to engage the right stakeholders to help setting Data Mining Success criteria to achieve the business goals.Machine Learning and Model building activities using Python or R are an important activities in any data science project. However, there are several other activities that are part of any Data Science project. The data needs to be prepared for application of machine learning techniques. There are lot of steps involved in preparing a data-set which would be suitable for achieving the business goal of the data science project. In this course, we are going to take a broader look and identify how each activity of CRISP-DM fits together towards achieving business outcomes of a data science project.The course lists the monitoring, reporting and user training needs during the execution of data science project. The data science project needs to conclude with deployment of data mining results and review of lessons learned. All the above activities are sequenced in this course along with their purpose and elaborate details for end to end execution of a data science project.
