|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/datasci-capstone
课程评论：没有评论
课程名称：大规模数据科学 - 毕业项目 概述：在毕业项目中，学生将参与一个实际项目，要求应用整个数据科学流程中的技能，包括数据准备、组织和转换、模型构建及结果评估。通过与Coursolve的合作，每个毕业项目都与对结果有直接利益关系并渴望在实践中应用的合作伙伴相关联。这些项目并不简单，结果也并未预设——你需要能够接受不确定性和负面结果！但我们相信，这种经历将带来丰厚的回报，并更好地为你在实践中进行数据科学项目奠定基础。 课程大纲： 第一部分：项目A：斗争杂物 描述：在此项目中，您将建立一个模型，以预测何时一栋建筑可能被认定为不适用。数据是真实的，问题是真实的，影响也是现实的。 第二部分：第二周：生成建筑物列表 描述：您将获得带有位置信息的事件集合；需要利用一些假设将这些事件按位置分组，以识别具体建筑。 第三部分：第三周：构建训练数据集 描述：通过与许可证数据关联每栋建筑的真实标签来构建一个训练集。 第四部分：第四周：训练和评估简单模型 描述：使用基本特征集来训练和评估一个简单的模型。 第五部分：第五周：特征工程 描述：衍生其他特征并重新训练，以提高模型的有效性。 第六部分：第六周：最终报告 描述：提交最终报告以进行评估。
Part: 1
Title:Project A: Blight Fight
Description:In this project, you will build a model to predict when a building is likely to be condemned. The data is real, the problem is real, and the impact is real.
Part: 2
Title:Week 2: Derive a list of buildings
Description:You are given sets of incidents with location information; you need to use some assumptions to group these incidents by location to identify specific buildings.
Part: 3
Title:Week 3: Construct a training dataset
Description:Construct a training set by associating each of your buildings with a ground truth label derived from the permit data.
Part: 4
Title:Week 4: Train and evaluate a simple model
Description:Use a trivial feature set to train and evaluate a simple model
Part: 5
Title:Week 5: Feature Engineering
Description:Derive additional features and retrain to improve the efficacy of your model.
Part: 6
Title:Week 6: Final Report
Description:Enter your final report for grading.
In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Through a collaboration with Coursolve, each Capstone project is associated with partner stakeholders who have a vested interest in your results and are eager to deploy them in practice. These projects will not be straightforward and the outcome is not prescribed -- you will need to tolerate ambiguity and negative results! But we believe the experience will be rewarding and will better prepare you for data science projects in practice.
