Data Science Fellowship Project - Finance with Curie Labs

FinTech remains one of the top hiring industries for Data Science. Standing out in the job market requires a portfolio of work around several important problems, one such being

1. Probability of Default
2. Credit Score Analysis

How is it different from a Kaggle Project or YouTube Project:

1. Dataset is NOT PUBLICLY available and is created by mentors that contains real world features.
2. We will look into industry-standard techniques on reducing dimensions
3. We will check out various trade-offs to select a ML technique for prediction of PD and Scorecard Development.
4. We will dive into Deployment using AWS Airflow.
5. We will observe how the post deployment processes are done:
6. Model Monitoring
7. Model Benchmarking & Stress-testing
8. And if the model is not stable, we will see Model re-tuning & recalibration
9. Create Technical document on Google Docs or Notion following guidelines.

We are booked for 30th Nov'2024 cohort. Next batch will start on 28th Dec'2024.