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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-analytics-business-capstone
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课程名称：高级商业分析顶点课程 课程概述：分析过程是涉及一系列相互关联的活动，以帮助做出更好的决策并提升商业绩效。本课程的顶点项目旨在让学员体验这个过程，项目将在数据、分析与模型的基础上，最终呈现出洞察结果。 在本顶点项目中，学员将分析金融贷款数据，以协助投资公司做出投资决策。课程将涵盖数据理解与清理、数据分析和分析结果展示等数据分析项目的典型步骤。 课程大纲： 模块1 - 理解数据并为分析做好准备：本周目标是理解数据并为分析做好准备，数据预处理和清理是分析项目的第一步，至关重要。 模块2 - 执行预测分析任务：本周将进行一些预测分析任务，包括贷款分类和预测违约贷款的损失，使用多种工具和技术提升模型预测准确性。 模块3 - 使用处方分析工具提供投资基金分配建议：本周关注处方分析，使用聚类和基于模拟的优化工具提供投资资金的分配建议，分配资金的智慧将直接影响投资组合的财务回报。 模块4 - 向客户展示分析结果：在最后一周，学员将展示自己的分析结果，重点选择展示内容，并应用课程中学习的沟通技巧。 该课程将为您提供全面的商业分析实战经验，助力您的职业发展。
Name:Module 1 - Understand the data and prepare your data for analysis
Description:This week your goal is to understand the data and prepare the data for analysis. As we discussed in this specialization, data preprocessing and cleanup is often the first step in data analytics projects. Needless to say, this step is crucial for the success of this project. We've selected a few videos from Courses 2 and 4 for you to review before completing this week's assignments. Dealing With Missing Values and Dealing with Outliers videos will remind you how to perform preliminary data cleanups. The last part of the assignments ask you to construct data visualizations. You may find the ideas discussed in What is Good Data Visualization? and Graphical Excellence useful.
Name:Module 2 - Perform predictive analytics tasks
Description:This week you will perform some predictive analytics tasks, including classifying loans and predicting losses from defaulted loans. You will try a variety of tools and techniques this week, as the predictive accuracy of different tools can vary quite a bit. It is rarely the case that the default model produced by ASP is the best model possible. Therefore, it is important for you to tune the different models in order to improve the performance.This week’s assignments require you to build predictive models for both classification and regression tasks.
Before working on the assignments, you may review a few videos to remind yourself several important concepts, such as cross validation. These concepts are discussed in the videos Cross Validation and Confusion Matrix and Assessing Predictive Accuracy Using Cross-Validation. You may also find a refresher on XLMiner useful. The videos Building Logistic Regression Models using XLMiner and How to Build a Model using XLMiner discuss how to build logistic regression and linear regression models. Depending on your needs, you may also go back to the videos that discuss how to build trees and neural networks.
Name:Module 3 - Provide suggestions on how to allocate investment funds using prescriptive analytics tools
Description:This week we turn our attention to prescriptive analytics, where you will provide some concrete suggestions on how to allocate investment funds using analytics tools, including clustering and simulation-based optimization. You will see that allocating funds wisely is crucial for the financial return of the investment portfolio.
The relevant videos for this week are from Course 3: Week 1: Cluster analysis with XLMiner, Week 2: Adding uncertainty to spreadsheet model, Week 2: Defining output variables and analyzing results.
Name:Module 4 - Present your analytics results to your clients
Description:You have done a lot so far! In this last week, you will present to your analytics results to your clients. Since you have many results in your project, it is important for you to judiciously choose what to include in your presentation. Several videos in Course 4 offer some guidelines on communicating analytics results. This assignment will give you an opportunity to apply the skills you learned there. Good luck!
The analytics process is a collection of interrelated activities that lead to better decisions and to a higher business performance. The capstone of this specialization is designed with the goal of allowing you to experience this process. The capstone project will take you from data to analysis and models, and ultimately to presentation of insights. In this capstone project, you will analyze the data on financial loans to help with the investment decisions of an investment company. You will go through all typical steps of a data analytics project, including data understanding and cleanup, data analysis, and presentation of analytical results. For the first week, the goal is to understand the data and prepare the data for analysis. As we discussed in this specialization, data preprocessing and cleanup is often the first step in data analytics projects. Needless to say, this step is crucial for the success of this project. In the second week, you will perform some predictive analytics tasks, including classifying loans and predicting losses from defaulted loans. You will try a variety of tools and techniques this week, as the predictive accuracy of different tools can vary quite a bit. It is rarely the case that the default model produced by ASP is the best model possible. Therefore, it is important for you to tune the different models in order to improve the performance. Beginning in the third week, we turn our attention to prescriptive analytics, where you will provide some concrete suggestions on how to allocate investment funds using analytics tools, including clustering and simulation based optimization. You will see that allocating funds wisely is crucial for the financial return of the investment portfolio. In the last week, you are expected to present your analytics results to your clients. Since you will obtain many results in your project, it is important for you to judiciously choose what to include in your presentation. You are also expected to follow the principles we covered in the courses in preparing your presentation.
