$A_x$
Actuarial Tutor Training & Consulting
TH
Corporate & Data Science Level: Intermediate

Python & Data Science for Actuaries & Insurance Analytics

Python และ Data Science สำหรับนักคณิตศาสตร์ประกันภัยและการวิเคราะห์ประกัน

Comprehensive hands-on training for actuarial and analytics teams modernizing from Excel to Python: pandas, polars, chainladder-python, automated pricing workflows, and stochastic loss simulation.

Automated Loss Development Factor Pipeline in Python
LDFk=∑i=1n−kCi,k+1∑i=1n−kCi,kLDF_k = \frac{\sum_{i=1}^{n-k} C_{i,k+1}}{\sum_{i=1}^{n-k} C_{i,k}}

Course Registration & Inquiry

Available as individual mentorship, group cohort, or in-house corporate training tailored to your team's dataset.

Lecturer Yodharin Monplub
Format Online / On-site / Hybrid
Prerequisites Basic Statistics / Excel
Enroll via LINE (@actuarialtutor) Email Course Inquiry

Comprehensive Course Syllabus & Learning Modules

1

1. Modern Python Environment for Actuarial Work

  • Setting up reproducible virtual environments (uv, pip, conda, VS Code)
  • Data structures for insurance: fast tabular processing with Pandas and Polars
  • Vectorized operations, performance profiling, and memory optimization for big claims data
2

2. Automated Reserving & Triangle Modeling in Python

  • Building incremental and cumulative loss triangles programmatically
  • Implementing Chain-Ladder, Bornhuetter-Ferguson, and Cape Cod using chainladder-python
  • Stochastic reserving: Mack variance estimation and bootstrapping prediction error intervals
  • Exporting auditable reporting tables and regulatory Excel packages directly from code
3

3. Actuarial Modeling & Statistical Pipelines

  • Mortality, lapse, and survival analysis using lifelines (Kaplan-Meier and Cox Proportional Hazards)
  • Frequency and severity modeling using GLMs (statsmodels: Poisson, Negative Binomial, Gamma)
  • Simulating aggregate portfolio losses using Monte Carlo and Panjer recursion
4

4. Interactive Dashboards & Workflow Automation

  • Building interactive internal actuarial web applications with Streamlit
  • Automated monthly reserve reconciliation and variance reporting pipelines
  • Version control with Git for collaborative actuarial peer reviews
5

5. Corporate In-House Case Study

  • Migrating a corporate month-end reserving Excel workbook into an automated Python pipeline
  • Stress testing insurance liabilities under fluctuating economic scenarios