| Academic Unit: |
Industrial Engineering |
| Mode of Delivery: |
Face to face |
| Prerequisites: |
CMPE140 |
| Language of Instruction: |
English |
| Level of Course Unit: |
Undergraduate |
| Course Coordinator: |
- - |
| Course Objectives: |
• To introduce students to data storage systems and SQL for gathering data from relational databases;
• To introduce students to the concept and techniques of data mining. Specifically, to teach descriptive and prescriptive models and their applications in R Gui;
To have students apply their knowledge of statistics in the solution of industrial problems using data. |
| Course Contents: |
This course is an introduction to data analytics and data mining techniques for industrial engineers using an open source data mining program R Gui and SQL. Students will learn necessary tools for gathering data from a relational database, data visualization, data mining algorithms and their applications in R. |
| Learning Outcomes of the Course Unit (LO): |
- 1- Visual representations of datasets and fundamental statistical analysis in R;
- 2- Understanding relational databases, creating queries in SQL for retrieving data to R Gui;
- 3- Fundamental data mining techniques for descriptive modeling and their applications in R Gui;
- 4- Techniques for predictive modeling and applications in R;
- 5- Designing, testing, and implementing algorithmic solutions for problems in industrial engineering and developing a data mining project;
|
| Planned Learning Activities and Teaching Methods: |
• Homework (3), • Laboratory Work (6), • Computer Use (R Studio, SQL), • Midterm Exam (1), • Project (1), • Final Exam (1). |
| Week | Subjects | Related Preperation |
| 1 |
Fundamenals of Data Mining and R Programming |
|
| 2 |
R Programming and SQL |
|
| 3 |
Data Storage and Management in Relational Databases |
Review of the previous week’s lab exercises |
| 4 |
Descriptive Modeling: Distance Measures |
Preliminary reading of the lecture material. |
| 5 |
Descriptive Modeling: K-Means Algorithm |
|
| 6 |
Descriptive Modeling: Hierarchical Clustering |
|
| 7 |
Descriptive Modeling: Hierarchical Clustering |
|
| 8 |
Introduction to Predictive Models |
|
| 9 |
Predictive Modeling: Classification Tree |
|
| 10 |
Predictive Modeling: Linear Discriminant |
|
| 11 |
Simple Linear Regression in R |
|
| 12 |
Generalized Linear Regression Models |
|
| 13 |
Artificial Neural Networks |
|
| 14 |
Artificial Neural Networks |
|
At Kadir Has University, a Semester is 14 weeks; The weeks 15 and 16 are reserved for final exams.
THE RELATIONSHIP BETWEEN COURSE LEARNING OUTCOMES (LO) AND PROGRAM QUALIFICATIONS (PQ)
| # |
PQ1 |
PQ2 |
PQ3 |
PQ4 |
PQ5 |
PQ6 |
PQ7 |
PQ8 |
PQ9 |
PQ10 |
PQ11 |
PQ12 |
| LO1 |
|
|
|
|
|
|
|
|
|
|
|
|
| LO2 |
|
|
|
|
|
|
|
|
|
|
|
|
| LO3 |
|
|
|
|
|
|
|
|
|
|
|
|
| LO4 |
|
|
|
|
|
|
|
|
|
|
|
|
| LO5 |
|
|
|
|
|
|
|
|
|
|
|
|
Contribution: 1 Low, 2 Average, 3 High
