Course curriculum

    1. 1. Business Context & Introduction

    2. Course Materials

    3. 2. Data Architecture Model

    4. 3. Source System Investigation

    5. 4. MySQL Installation

    6. 5. Apache Nifi Installation

    7. 6. Anaconda, Python & Jupyter Notebook Installation

    8. 7. Data Warehouse & Datamart Creation

    9. 8. Apache Nifi: Customer, Account, ... Data warehouse Loading

    10. 9. MySQL Data Warehouse Investigation

    11. 10. Apache Nifi: Customer DataMart Loading

    12. 11. Machine Learning Model: Business Problem & Import Datasets

    13. 12. Machine Learning Model: Data Visualization & Create Training and Testing Data

    14. 13. XGBoost Algorithm Understanding

    15. 14. XGBoost Model Development

    16. 15. Artificial Neural Network (ANN) Algorithm Understanding

    17. 16. ANN Model Development

    18. 17. Packaging & Loading the trained (XGBoost & ANN) model

    19. Special Gift For You (Bonus)

About this course

  • $9.90
  • 19 lessons
  • 3 hours of video content

Customer Spending Forecasting - Insurance Policy Case Size Prediction

📘 What You’ll Learn:

  • 🔍 Predicting Insurance Policy Case Size: Develop skills to forecast the amount customers are likely to pay for insurance.
  • 🏢 Banking & Insurance Data Integration: Work within a bancassurance model alongside roles like Data Architect, Data Scientist, Data Engineer, and more.
  • 💾 ETL & Data Warehousing: Extract, transform, and load data into data warehouses and marts using Apache NiFi, MySQL, and more.
  • 🤖 Machine Learning Application: Build and deploy models (XGBoost and ANNs) to forecast insurance policy case sizes with Jupyter Notebook.

📝 Requirements:

  • 🖥️ Basic understanding of Data Analytics and Python
  • 💼 Interest in banking and insurance
  • 📈 Familiarity with ETL processes and machine learning fundamentals

📖 Description:

This course dives into predictive modeling for Insurance Policy Case Size. Learn to gather and centralize customer demographic and financial data, and work across different roles to complete the full data pipeline: from analysis to deployment. Engage with ETL processes, data warehousing, and AI model training to provide accurate forecasts, helping banks and insurance companies in customer targeting and decision-making. A hands-on, cross-functional experience in banking and insurance analytics!

👥 Who This Course is For:

  • 📊 Data Scientists and Analysts interested in financial data applications
  • 🧑‍💼 Banking and Insurance Professionals looking to leverage data for insights

Instructor(s)

Instructor Bio:

I'm a seasoned author deeply passionate about data strategy and management. With over 15 years in IT, spanning banking, insurance, and retail, I've held key roles such as Data Division Director and Chief Data Officer. Currently, as Director of Data Division, I shape data initiatives, while as a Governance & Analytics expert, I establish robust frameworks. Through my writing, I share practical strategies and insights honed from diverse industry experience.

Dien Pham Cong

Chief Data Officer

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