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In this course, you will :
- Analyze data on financial loans to aid an investment firm's investment decisions. You will go through all of the typical steps of a data analytics project, such as data understanding and cleanup, data analysis, and data presentation.
- The goal for the first week is to understand the data and prepare it for analysis. Data preprocessing and cleanup, as discussed in this specialisation, is frequently the first step in data analytics projects. Needless to say, this step is critical to the project's success.
- In the second week, you will perform predictive analytics tasks such as loan classification and loss prediction from defaulted loans. This week, you will experiment with a variety of tools and techniques because the predictive accuracy of different tools varies greatly. 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.
Syllabus :
- Understand the data and prepare your data for analysis
- Perform predictive analytics tasks
- Provide suggestions on how to allocate investment funds using prescriptive analytics tools
- Present your analytics results to your clients
