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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/advanced-data-science-capstone
课程评论：没有评论
课程名称：高级数据科学顶点项目 课程概述：该课程旨在培养学员对大规模并行数据处理、数据探索与可视化、高级机器学习和深度学习的深刻理解，并能够将所学知识应用于实际案例中。在课程中，学员需要合理地阐述架构决策，理解不同算法、框架和技术的特征及其对模型性能和可扩展性的影响。 请注意：课程结束时需制作一个短视频演示，这是通过课程的必要条件，视频无需公开分享。 课程大纲： 第一部分：第一周 - 确定数据集和用例 描述：本模块介绍本顶点项目所使用的基本过程模型，学员需识别一个实际的应用用例和数据集。 第二部分：第二周 - 数据提取、转换与加载（ETL）及特征创建 描述：本模块强调ETL、数据清洗和特征创建在每个数据科学项目中的重要性，作为前期准备工作。 第三部分：第三周 - 模型定义与训练 描述：本模块强调基于用例和数据集进行模型选择的重要性，理解这两者如何影响有效模型算法的选择。 第四部分：模型评估、调优、部署与文档编写 描述：一旦模型训练完成，使用合适的指标评估其性能至关重要。此外，模型完成后需要以适当的方式使业务相关者能够使用。
Part: 1
Title:Week 1 - Identify DataSet and UseCase
Description:In this module, the basic process model used for this capstone project is introduced. Furthermore, the learner is required to identify a practical use case and data set
Part: 2
Title:Week 2 - ETL and Feature Creation
Description:This module emphasizes on the importance of ETL, data cleansing and feature creation as a preliminary step in ever data science project
Part: 3
Title:Week 3 - Model Definition and Training
Description:This module emphasizes on model selection based on use case and data set. It is important to understand how those two factors impact choice of a useful model algorithm.
Part: 4
Title:Model Evaluation, Tuning, Deployment and Documentation
Description:One a model is trained it is important to assess its performance using an appropriate metric. In addition, once the model is finished, it has to be made consumable by business stakeholders in an appropriate way
This project completer has proven a deep understanding on massive parallel data processing, data exploration and visualization, advanced machine learning and deep learning and how to apply his knowledge in a real-world practical use case where he justifies architectural decisions, proves understanding the characteristics of different algorithms, frameworks and technologies and how they impact model performance and scalability. Please note: You are requested to create a short video presentation at the end of the course. This is mandatory to pass. You don't need to share the video in public.
