1. Please check https://yiduai.sg/data-science-meets-fintech/ for detailed information.
2. Why This Program
1). Learn from Experts in Fintech:
4-week course on data science and fintech from excellent instructors and TAs.
2). Fully Hands-on:
4 in-class projects and 4 after-class assignments.
3). Project Competition with Awards:
There will be a final course project competition with real-world dataset. Top 3 Winner teams will be awarded S$[masked].
4). Job Opportunity:
Good students will have the chance to get hired by Wecash as full-time data scientist or interns.
Student will have chance to communicate with YiDu AI community (Thousands of top AI researchers and data scientists).
1). 4 Weekend courses: 1.5 hours teaching + 1.5 hours hands-on in-class project
2). Hands-on Exercises: We will prepare in-class project every week. Students will have opportunities to work with assistance from TAs.
3). Course Project: There will be a final course project competition with real-world dataset. Top 3 Winner teams will be awarded S$[masked].
Week 1: Introduction to Data Science in Fintech (23rd Feb)
Overview of data driven applications in FinTech
Microfinance business lifecycle
Survey of scorecard model
Performance evaluation of scorecard
Backtest of scorecard
Online monitoring of scorecard
Lendingclub data exploration
Demo of scorecard implementation
Week 2: Scorecard Model For Microfinance (2nd Mar)
Overview of machine learning models for scorecard
Boosting tree method
Imbalance data in microfinance
Overfitting, Activation functions, regularization
Introduction to xgboost package
Implement scorecard model using xgboost
Week 3: Feature Engineering and Parameter Tuning (9th Mar)
Possible data source for scorecard
Data cleaning and transformation
Code demo of feature selection
Review of xgboost parameter
Introduction to skopt package
Code demo of Bayesian optimization
Week 4: Location, Text and Graph Data Analysis (16th Mar)
Overview of big data in Fintech
Location data for microfinance
Gaussian process for spatial data analysis
Text and graph data for microfinance
Label propagation in social graph
Graph embedding method
Code demo of Gaussian process
Demo of extracting financial sensitive text
Demo of graph embedding
With the content learnt in class, you are supposed to implement:
1). Data cleaning
2). Feature engineering
3). Feature selection
4). Model design
5). Parameter tuning
6). Risk calibration
7). Model evaluation
Dr. Ouyang Ruofei
Data scientist from Wecash APAC. He works on credit risk modeling in consumer lending business which serves millions of customers from Indonesia, India, and Vietnam. Prior to this position, he is a research fellow in Risk Management Instituite of NUS focusing on financial derivative pricing model using machine learning.
5. Target Participants
Anyone who is interested in Data Science and Fintech.
Please fill out the your basic information in the application form and make the payment via this link: https://www.eventbrite.sg/e/data-science-meets-fintech-23rd-feb-16th-mar-tickets-54443941294. We will come back to you soon.
Timeline: The course will happen in each Saturday starting from 23rd February until 16th March.
Program fees: S$ 499. We accept at most 40 students for this program. Please register early if you want to attend.
Early Bird Price (first 10 registrants): S$ 459.
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