Course Information

Course Information
Course Title Code Language Type Semester L+U Hour Credits ECTS
Probability And Statistics BM106 Turkish Compulsory 2. Semester 3 + 0 3.0 4.0
Prerequisite Courses
Course Level Undergraduate
Mode of delivery Lecture, Presentation, Problem Solving, Discussion
Course Coordinator Prof. Dr. ALİ ÇALHAN
Instructor(s)
Goals Teaching probability theory and statistics
Course Content Probability theory, random variables, variable families, statistics topics
Learning Outcomes
# Öğrenme Kazanımı
1 To be able to understand and relate the concepts of probability theory and set theory
2 To be able to comprehend probability axioms, conditional probability and bayes' theorem
3 To be able to understand the subject of events and independent events, to use sequential experiments and tree diagrams.
4 Understanding of counting methods and independent trials
5 To be able to understand the concepts of random variable and discrete random variable
6 To be able to understand the mass function and cumulative distribution function of discrete random variable families.
7 Ability to calculate mean, expected value, moment, variance and standard deviation in discrete random variables
8 To be able to understand the density function and cumulative distribution function of continuous random variable families.
9 Ability to calculate mean, expected value, moment, variance and standard deviation values in continuous random variables
10 To be able to comprehend the concepts of correlation and covariance with functions of random variable pairs.
Lesson Plan (Weekly Topics)
Week Topics/Applications Method
1. Week set theory and probability theory Preparation, After Class Study Research Other Activities Presentation (Preparation) Interview Practice Class Hours
2. Week Probability axioms, conditional probability and Bayes' theorem Interview Practice Class Hours Preparation, After Class Study Presentation (Preparation) Other Activities Research
3. Week Independent events, sequential experiments, and tree diagrams Research Practice Other Activities Presentation (Preparation) Class Hours Interview Preparation, After Class Study
4. Week Counting methods and independent trials Interview Class Hours Practice Other Activities Preparation, After Class Study Research Presentation (Preparation)
5. Week Random variable concept and discrete random variables Research Preparation, After Class Study Practice Interview Class Hours Presentation (Preparation) Other Activities
6. Week Discrete random variable families Presentation (Preparation) Class Hours Preparation, After Class Study Practice Research Interview Other Activities
7. Week Probability mass function and cumulative distribution function in discrete random variables Preparation, After Class Study Class Hours Other Activities Practice Research Interview Presentation (Preparation)
8. Week Mean, expected value, moment, variance and standard deviation in discrete random variables Presentation (Preparation) Practice Interview Preparation, After Class Study Research Class Hours Other Activities
9. Week Continuous random variables Preparation, After Class Study Practice Interview Class Hours Presentation (Preparation) Research Other Activities
10. Week Probability density function and cumulative distribution function in continuous random variables Other Activities Research Interview Practice Presentation (Preparation) Class Hours Preparation, After Class Study
11. Week Continuous random variable families Presentation (Preparation) Other Activities Class Hours Research Preparation, After Class Study Interview Practice
12. Week Mean, expected value, moment, variance and standard deviation in continuous random variables Presentation (Preparation) Preparation, After Class Study Class Hours Research Interview Practice Other Activities
13. Week Random variable pairs and expected value Other Activities Preparation, After Class Study Presentation (Preparation) Interview Practice Class Hours Research
14. Week Functions, correlation and covariance of random variable pairs Interview Practice Presentation (Preparation) Other Activities Preparation, After Class Study Research Class Hours
*Midterm and final exam dates are not specified in the 14-week course operation plan. Midterm and final exam dates are held on the dates specified in the academic calendar with the decision of the University Senate.
The Matrix for Course & Program Learning Outcomes
No Program Requirements Level of Contribution
1 2 3 4 5
1 Adequate knowledge of mathematics, science and related engineering disciplines; Ability to use theoretical and applied knowledge in these fields in complex engineering problems
2 Ability to identify, define, formulate and solve complex engineering problems; for this purpose, the ability to select and apply appropriate analysis and modeling methods
3 Knowledge and awareness about the management, control, development and security/reliability of Information Technologies
4 Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; for this purpose, the ability to apply modern design methods
5 Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology, and constantly renew oneself
6 Ability to design and conduct experiments, collect data, analyze and interpret results for the investigation of complex engineering problems or discipline-specific research topics
7 Ability to work effectively in disciplinary and multi-disciplinary teams; individual study skills
8 Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering practice; Ability to use information technologies effectively
9 Knowledge of the effects of engineering practices on health, environment and safety in universal and social dimensions and the problems of the age reflected in the field of engineering; awareness of the legal consequences of engineering solutions
10 Ability to communicate effectively in Turkish orally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions
11 Behaving in accordance with ethical principles, awareness of professional and ethical responsibility; information about standards used in engineering applications
12 Knowledge of business practices such as project management, risk management and change management; awareness of entrepreneurship, innovation; information about sustainable development
Relations with Education Attainment Program Course Competencies
Program Requirements DK1 DK2 DK3 DK4 DK5 DK6 DK7 DK8 DK9 DK10
PY1 5 5 5 5 5 5 5 5 5 5
PY2 5 5 5 5 5 5 5 5 5 5
PY3 2 2 2 2 2 2 2 2 2 2
PY4 4 4 4 4 4 4 4 4 4 4
PY5 2 2 2 2 2 2 2 2 2 2
PY6 5 5 5 5 5 5 5 5 5 5
PY7 3 3 3 3 3 3 3 3 3 3
PY8 5 5 5 5 5 5 5 5 5 5
PY9 2 2 2 2 2 2 2 2 2 2
PY10 2 2 2 2 2 2 2 2 2 2
PY11 3 3 3 3 3 3 3 3 3 3
PY12 2 2 2 2 2 2 2 2 2 2
Recommended Sources
Ders Kitabı veya Notu Ders Kitabı veya Ders Notu bulunmamaktadır.
Diğer Kaynaklar
  • Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers (Roy D. Yates, David J. Goodman)
ECTS credits and course workload
ECTS credits and course workload Quantity Duration (Hour) Total Workload (Hour)
Ders İçi
Class Hours 3 14 42
Ders Dışı
Preparation, After Class Study 1 14 14
Presentation (Preparation) 1 14 14
Practice 1 14 14
Sınavlar
Midterm 1 1 1 1
Final 1 1 1
Practice 1 1 1
Practice End-Of-Term 1 1 1
Classroom Activities 1 14 14
Total Workload 102
*AKTS = (Total Workload) / 25,5 ECTS Credit of the Course 4.0