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Syllabus ( IE 512 )

   Basic information
Course title: Advanced Statistical Modeling and Data Mining
Course code: IE 512
Lecturer: Assist. Prof. Kemal Dinçer DİNGEÇ
ECTS credits: 7.5
GTU credits: 3 (3+0+0)
Year, Semester: 1/2, Spring
Level of course: Second Cycle (Master's)
Type of course: Area Elective
Language of instruction: English
Mode of delivery: Face to face
Pre- and co-requisites: none
Professional practice: No
Purpose of the course: This course aims to teach advanced statistical modelling subjects such as multiple regression and logistic regression, and as well as data mining concepts and models such as data management, causal modelling, bayesian learning, neural networks and clustering.
   Learning outcomes Up

Upon successful completion of this course, students will be able to:

  1. Make regression analysis.

    Contribution to Program Outcomes

    1. Increase his/her knowledge level about Operations Research, Management Sciences and Production Management.
    2. Acquire scientific knowledge

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Term paper
  2. Apply a decision tree model.

    Contribution to Program Outcomes

    1. Increase his/her knowledge level about Operations Research, Management Sciences and Production Management.
    2. Acquire scientific knowledge

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Seminar/presentation
    4. Term paper
  3. Model neural networks.

    Contribution to Program Outcomes

    1. Increase his/her knowledge level about Operations Research, Management Sciences and Production Management.
    2. Acquire scientific knowledge

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Seminar/presentation
    4. Term paper
  4. Model and apply clustering algorithms

    Contribution to Program Outcomes

    1. Increase his/her knowledge level about Operations Research, Management Sciences and Production Management.
    2. Acquire scientific knowledge

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Seminar/presentation
    4. Term paper
  5. Perform association mining.

    Contribution to Program Outcomes

    1. Increase his/her knowledge level about Operations Research, Management Sciences and Production Management.
    2. Acquire scientific knowledge

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Seminar/presentation
    4. Term paper
  6. Perform sequence mining.

    Contribution to Program Outcomes

    1. Increase his/her knowledge level about Operations Research, Management Sciences and Production Management.
    2. Acquire scientific knowledge

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Seminar/presentation
    4. Term paper
   Contents Up
Week 1: Managing Data
Week 2: Multiple Regression Models
Week 3: Logistic Regression
Week 4: Causality: Theoretical Foundations
Week 5: Causal Modelling
Week 6: Bayesian Learning
Week 7: Decision Tree Models, Midterm
Week 8: Artificial Neural Networks
Week 9: Artificial Neural Networks based Advanced Techniques
Week 10: Statistical Learning
Week 11: Clustering: Basic Concepts
Week 12: Clustering Techniques
Week 13: Association Mining
Week 14: Sequence Mining
Week 15*: -
Week 16*: Final exam
Textbooks and materials:
Recommended readings: 1. Data Mining Concepts and Techniques, Jiawei Han and Micheline Kamber, Academic Press, 2001.
2. Causality Models, Reasoning and Inference, Judea Pearl, second edition, Cambridge University Press, 2009.
  * Between 15th and 16th weeks is there a free week for students to prepare for final exam.
Assessment Up
Method of assessment Week number Weight (%)
Mid-terms: 7 30
Other in-term studies: 0
Project: 14 15
Homework: 3,5,6,7,9,12,13,14 10
Quiz: 3,6,9,12 5
Final exam: 16 40
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 3 14
Own studies outside class: 5 14
Practice, Recitation: 0 0
Homework: 3 8
Term project: 3 10
Term project presentation: 1 1
Quiz: 0.5 4
Own study for mid-term exam: 0 0
Mid-term: 0 0
Personal studies for final exam: 7 3
Final exam: 3 1
    Total workload:
    Total ECTS credits:
*
  * ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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