Course Code	Semester	Course Name	LE/RC/LA	Course Type	Language of Instruction	ECTS
MCB1007	1	Introduction to Probability and Statistics	4/0/0	BSC	English	6

Course Goals	Upon completion of this course, students are expected to understand and apply basic concepts in probability theory and mathematical statistics.
Prerequisite(s)	NONE
Corequisite(s)	NONE
Special Requisite(s)	Read, understand, formulate, explain, and apply mathematical statements, and state and apply important results in key mathematical areas (Calculus I and Calculus II).
Instructor(s)	Assist. Prof. Dr. Fatih Uçar
Course Assistant(s)	NONE
Schedule	Section A: Mon 09:00-11:00, Wed 13:00-15:00, Section B: Mon 11:00-13:00, Wed 15:00-17:00
Office Hour(s)	Tuesday 11:00-13:00, 3-A-15
Teaching Methods and Techniques	Lectures and recitation.
Principle Sources	Lecture notes, http://web.iku.edu.tr/~eyavuz. I. Miller, M. Miller, John E. Freund's Mathematical Statistics with Applications, Pearson Prentice Hall, Seventh Edition, New Jersey, ISBN: 0978-0-13-124646-1, 2004.
Other Sources	Murray R. Spiegel, John J. Schiller, and R. Alu Srinivasan, Probability and Statistics, Schaum's Outline Series, Third Edition, New York, ISBN: 978-0-07-154426-9, 2009.

Course Schedules
Week	Contents	Learning Methods
1. Week	Sets, Combinatorial Methods, Binomial Coefficients	Lectures and recitation
2. Week	Sample Spaces, The Probability of an Event, Some Rules of Probability, Conditional Probability, Independent Event, Bayes' Theorem	Lectures and recitation
3. Week	Random Variables, Discrete Probability Distributions	Lectures and recitation
4. Week	Continuous Random Variables, Multivariate Distributions,	Lectures and recitation
5. Week	Marginal Distributions, Conditional Distributions	Lectures and recitation
6. Week	The Expected Value of a Random Variable, Moments	Lectures and recitation
7. Week	Chebyshev’s Theorem, Moment Generating Functions, Product Moments, Conditional Expectation	Lectures and recitation
8. Week	The Discrete Uniform Distribution, The Bernoulli Distribution, The Binomial Distribution, The Negative Binomial and Geometric Distribution	Lectures and recitation
9. Week	The Hypergeometric Distribution, The Poisson Distribution	Lectures and recitation
10. Week	The Uniform Distribution, The Normal Distribution	Lectures and recitation
11. Week	The Normal Approximation to the Binomial Distribution, The Normal Approximation to the Poisson Distribution	Lectures and recitation
12. Week	Population and Sample. Statistical Inference, Sampling With and Without Replacement	Lectures and recitation
13. Week	Random Samples, The Sampling Distribution of the Mean, The Sampling Distribution of the Mean: Finite Population	Lectures and recitation
14. Week	Interval Estimation	Lectures and recitation
15. Week	Final week	Exam
16. Week	Final week	Exam
17. Week	Final week	Exam

Assessments
Evaluation tools	Quantity	Weight(%)
Midterm(s)	1	40
Homework / Term Projects / Presentations	1	20
Final Exam	1	40

Program Outcomes PO-1 Interpreting advanced theoretical and applied knowledge in Mathematics and Computer Science. PO-2 Critiquing and evaluating data by implementing the acquired knowledge and skills in Mathematics and Computer Science. PO-3 Recognizing, describing, and analyzing problems in Mathematics and Computer Science; producing solution proposals based on research and evidence. PO-4 Understanding the operating logic of computer and recognizing computational-based thinking using mathematics as a discipline. PO-5 Collaborating as a team-member, as well as individually, to produce solutions to problems in Mathematics and Computer Science. PO-6 Communicating in a foreign language, and interpreting oral and written communicational abilities in Turkish. PO-7 Using time effectively in inventing solutions by implementing analytical thinking. PO-8 Understanding professional ethics and responsibilities. PO-9 Having the ability to behave independently, to take initiative, and to be creative. PO-10 Understanding the importance of lifelong learning and developing professional skills continuously. PO-11 Using professional knowledge for the benefit of the society.

Learning Outcomes LO-1 Compute permutations and combinations. LO-2 Understand what a random variable is. LO-3 Define basic probability terminology, e.g., experiment, outcome, sample space, event, etc. LO-4 Describe a probability distribution and a probability density function. LO-5 Understand mathematical expectation LO-6 Explain joint, marginal and conditional probability distributions LO-7 Understand where/when special probability distribution functions should be used LO-8 Describe the various special continuous distributions. LO-9 Able to solve problems independently.

Course Assessment Matrix:

	PO 1	PO 2	PO 3	PO 4	PO 5	PO 6	PO 7	PO 8	PO 9	PO 10	PO 11
LO 1
LO 2
LO 3
LO 4
LO 5
LO 6
LO 7
LO 8
LO 9