Dr. John Jung-Woon Yoo

• References:
  • Fundamentals of Database Management Systems by Elmasri and Navathe, Pearson
  • Introduction to Data Mining by Tan, Steinbach, Karpatne, and Kumar, Pearson

This course provides a theoretical background in descriptive, predictive, and prescriptive analytics methods and their applications in engineering fields. It covers various artificial intelligence techniques for data mining, expert system design and implementation, and computing foundations for data management and data analytics, with specific applications to Production Planning and Control and Inventory Management.

Students will:

• Understand the theoretical aspects of descriptive, predictive, and prescriptive analytics.
• Gain knowledge in artificial intelligence techniques for data mining and expert system design.
• Develop skills in database design, implementation, and interface programming.
• Apply analytics methods in practical engineering contexts, particularly in Production Planning and Control and Inventory Management.

1. Database Design and Implementation

   • Entity-Relationship Data Model
   • Relational Data Model
   • Database Management Systems (DBMS)

2. Database Interface Programming

   • Structured Query Language (SQL)
   • Open Database Connectivity (ODBC)
   • Bill of Materials (BOM) Database

3. Descriptive Analytics and Applications

   • Similarity Analysis
   • Clustering (K-mean and Agglomerative Clustering Algorithm)
   • Association Rule Mining (Apriori Algorithm)

4. Predictive and Prescriptive Analytics and Applications

   • Inventory Control Applications based on MRP Database
   • Automated Planning

git clone https://github.com/ehvenga/ime568.git