Data Analysis in Civil Engineering

AIM

The main objective of this course is to provide students with the analytical perspective and computational capabilities (MATLAB/Python) necessary to preprocess, statistically model, and interpret real-world engineering data (e.g., streamflow, precipitation, wave characteristics, material properties, and structural health) using advanced spatial and temporal analysis techniques.

CONTENT

This course contains; Basic Statistical Concepts,Probability Theory, Probability Density Function and Cumulative Density Function,Probability Distributions in Engineering,Data Preprocessing and Dimensionality Reduction,Hypothesis Testing and Confidence Intervals,Doğrusal Regresyon ve Korelasyon,Multiple Regression and Curve Fitting,Error Metrics and Model Performance,Introduction to Time Series Analysis,Stochastic Processes and Temporal Dependence,Harmonic and Spectral Analysis of Time Series,Data Denoising,Spatial Data Analysis and Interpolation,Case Studies and Project Presentations.

1. 1

   Apply fundamental statistical concepts, probability distributions, and data preprocessing and dimensionality reduction techniques.

   Taught by: Discussion Method, Problem Solving Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

2. 2

   Develop and evaluate linear and nonlinear regression as well as curve fitting models

   Taught by: Discussion Method, Problem Solving Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

3. 3

   Analyze and model stochastic processes and temporal dependencies

   Taught by: Discussion Method, Problem Solving Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

4. 4

   Execute comprehensive data analysis workflows and computational pipelines using scientific computing tools (MATLAB/Python).

   Taught by: Discussion Method, Problem Solving Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

WEEKLY PLAN

1. WEEK 1

   Basic Statistical Concepts

   Preparation: Previewing book and lecture notes

2. WEEK 2

   Probability Theory, Probability Density Function and Cumulative Density Function

   Preparation: Previewing book and lecture notes

3. WEEK 3

   Probability Distributions in Engineering

   Preparation: Previewing book and lecture notes

4. WEEK 4

   Data Preprocessing and Dimensionality Reduction

   Preparation: Previewing book and lecture notes

5. WEEK 5

   Hypothesis Testing and Confidence Intervals

   Preparation: Previewing book and lecture notes

6. WEEK 6

   Doğrusal Regresyon ve Korelasyon

   Preparation: Previewing book and lecture notes

7. WEEK 7

   Multiple Regression and Curve Fitting

   Preparation: Previewing book and lecture notes

8. WEEK 8

   Error Metrics and Model Performance

   Preparation: Previewing book and lecture notes

9. WEEK 9

   Introduction to Time Series Analysis

   Preparation: Previewing book and lecture notes

10. WEEK 10

    Stochastic Processes and Temporal Dependence

    Preparation: Previewing book and lecture notes

11. WEEK 11

    Harmonic and Spectral Analysis of Time Series

    Preparation: Previewing book and lecture notes

12. WEEK 12

    Data Denoising

    Preparation: Previewing book and lecture notes

13. WEEK 13

    Spatial Data Analysis and Interpolation

    Preparation: Previewing book and lecture notes

14. WEEK 14

    Case Studies and Project Presentations

    Preparation: Previewing book and lecture notes

ASSESSMENT

• Rate of Midterm Exam to Success50%
• Rate of Final Exam to Success50%

WORKLOAD

READING

• Brunton, S. L., & Kutz, J. N. (2022). Data-driven science and engineering: Machine learning, dynamical systems, and forecasting (2nd ed.). Cambridge University Press. Trauth, M. H. (2021). MATLAB recipes for earth sciences (5th ed.). Springer. Gilat, A. (2016). MATLAB: An introduction with applications (6th ed.). John Wiley & Sons. Papoulis, A., & Pillai, S. U. (2002). Probability, random variables, and stochastic processes (4th ed.). McGraw-Hill.

TEACHING STAFF

• Lect.Dr. Cem YILMAZERCOORDINATOR
• Lect.Dr. Cem YILMAZER