ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, NON-THESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
EEE5022 Applied Statistics Spring 3 0 3 6
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Assoc. Prof. SAEID KARAMZADEH
Course Lecturer(s): Prof. Dr. SELİM ZAİM
Course Objectives: The course introduces fundamental topics in statistics and implements its applications to industrial, medical, financial, energy and similar type very large-size datasets to infer meaninful statistical results. The course is for gradute students with no significant background on this subject. Implementations will be performed on the open source statistical software R. Introduction to R programming will be given.

Learning Outputs

The students who have succeeded in this course;
I. Identify basic terms in statistics.
II. Gain ability to use and apply basic methods and programming tools used in statistics over various engineering disciplines.
III. Ability to explore data and its relationships.
IV. Ability to perform hypothesis testing for statistical problems.
V. Perform statistical inference over statistical data.

Course Content

Topics include: Introduction to R programming, Sampling, Data Exploration, Exploring Relationships, Probability, Random Variables and Probability Distributions, Estimation, Hypothesis Testing, Statistical Inference, Multiple Testing Correction, ANOVA, Analysis of Categorical Variables, Regression Analysis, Bayesian Analysis, Survival Analysis, Over Representation Analysis, Meta Analysis.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction
2) Introduction to R statistical programming
3) Term Project
4) Data Exploration with R
5) Visualizing and Summarizing Relationships
6) Probability and Random Variables
7) Estimation in datasets
8) Hypothesis Testing for various engineering applications
9) Statistical Inference over various large datasets
10) ANOVA
11) Analysis of Categorical Variables
12) Regression and Bayesian Analysis
13) Survival analysis
14) Over Representation Analysis

Sources

Course Notes: Principles of Applied Statistics (Paperback), by D. R. Cox, Christl A. Donnelly 2011 ISBN-10: 1107644453 | ISBN-13: 978-1107644458
References: Introductory Statistics with R Peter Dalgaard 2011 ISBN 978-0-387-79053-4

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance 0 % 0
Laboratory 0 % 0
Application 0 % 0
Field Work 0 % 0
Special Course Internship (Work Placement) 0 % 0
Quizzes 0 % 0
Homework Assignments 0 % 0
Presentation 0 % 0
Project 1 % 30
Seminar 0 % 0
Midterms 1 % 30
Preliminary Jury 0 % 0
Final 1 % 40
Paper Submission 0 % 0
Jury % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 30
PERCENTAGE OF FINAL WORK % 70
Total % 100

ECTS / Workload Table

Activities Number of Activities Workload
Course Hours 14 42
Laboratory
Application
Special Course Internship (Work Placement)
Field Work
Study Hours Out of Class 14 42
Presentations / Seminar
Project 1 30
Homework Assignments
Quizzes
Preliminary Jury
Midterms 1 40
Paper Submission
Jury
Final 1 50
Total Workload 204

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Have sufficient background and an ability to apply knowledge of mathematics, science, and engineering to identify, formulate, and solve problems of electrical and electronics engineering.
2) Be able to define, formulate and solve sophisticated engineering problems by choosing and applying appropriate analysis and modeling techniques and using technical symbols and drawings of electrical and electronics engineering for design, application and communication effectively.
3) Have an ability to design or implement an existing design of a system, component, or process to meet desired needs within realistic constraints (realistic constraints may include economic, environmental, social, political, health and safety, manufacturability, and sustainability issues depending on the nature of the specific design).
4) Elektrik ve elektronik mühendisliği yapabilmek ve yeni uygulamalara uyum gösterebilmek için gerekli yenilikçi ve güncel teknikler, beceriler, bilgi teknolojileri ve modern mühendislik araçlarını geliştirmek, seçmek, uyarlamak ve kullanmak.
5) Be able to design and conduct experiments, as well as to collect, analyze, and interpret relevant data, and use this information to improve designs.
6) Be able to function individually as well as to collaborate with others in multidisciplinary teams.
7) Be able to communicate effectively in English and Turkish (if he/she is a Turkish citizen).
8) Be able to recognize the need for, and to engage in life-long learning as well as a capacity to adapt to changes in the technological environment.
9) Have a consciousness of professional and ethical responsibilities as well as workers’ health, environment and work safety.
10) Have the knowledge of business practices such as project, risk, management and an awareness of entrepreneurship, innovativeness, and sustainable development.
11) Have the broad knowledge necessary to understand the impact of electrical and electronics engineering solutions in a global, economic, environmental, legal, and societal context.