M.SC. in Computer Engineering (Without Thesis)

MATH 504 | Course Introduction and Application Information

Course Name

Statistics

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
MATH 504	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Second Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
National Occupation Classification	-
Course Coordinator	-
Course Lecturer(s)	Doç. Dr. Gözde YAZGI TÜTÜNCÜ
Assistant(s)	-

Course Objectives

To provide an introduction to some concepts of probability and statistics with applications of business and economic problems.

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	will be able to describe statistical decision.
2	will be able to describe volatility by means of statistical terms.
3	will be able to explain risk and ways of reducing the risk.
4	will be able to analyze given set of data in detail.
5	will be able to construct efficient models and test them.
6	will be able to estimate unknown parameters to eliminate uncertainty.

Course Description

This course provides an introduction to statistics with financial applications. Statistical estimation and analysis techniques are provided and illustrated with financial problems.

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Related Preparation	Learning Outcome
1	Probability and statistical models	“Statistics and Finance” by David Ruppert, Springer, 1st Edition, 2004.
2	Applications	“Statistics and Finance” by David Ruppert, Springer, 1st Edition, 2004.
3	Random sample and its properties	“Statistics and Finance” by David Ruppert, Springer, 1st Edition, 2004.
4	Applications	“Statistics and Finance” by David Ruppert, Springer, 1st Edition, 2004.
5	Sampling distributions	“Statistics and Finance” by David Ruppert, Springer, 1st Edition, 2004.
6	Applications	“Statistics and Finance” by David Ruppert, Springer, 1st Edition, 2004.
7	Estimation methods with applications	“Statistics and Finance” by David Ruppert, Springer, 1st Edition, 2004.
8	Applications	“Statistics and Finance” by David Ruppert, Springer, 1st Edition, 2004.
9	Hypothesis testing	“Statistics and Finance” by David Ruppert, Springer, 1st Edition, 2004.
10	Applications	“Statistics and Finance” by David Ruppert, Springer, 1st Edition, 2004.
11	Regression and correlation	“Statistics and Finance” by David Ruppert, Springer, 1st Edition, 2004.
12	Applications	“Statistics and Finance” by David Ruppert, Springer, 1st Edition, 2004.
13	Analysis of variance	“Statistics and Finance” by David Ruppert, Springer, 1st Edition, 2004.
14	Applications	“Statistics and Finance” by David Ruppert, Springer, 1st Edition, 2004.
15	Semester Review
16	Final Exam

Course Notes/Textbooks

“Statistics and Finance” by David Ruppert, Springer, 1st Edition, 2004. Hard Cover ISBN: 978-0-387-20270-9

Suggested Readings/Materials

“Introduction to Mathematical Statistics” by Hogg, Craig, and McKean. Prentice Hall, 6th Edition, 2004. ISBN-13: 978-0130085078

“Mathematical Statistics: Basic Ideas and Selected Topics” by Bickel, and Doksum. Prentice Hall, 2nd Edition, 2014. ISBN-13: 978-1498723800

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5	LO 6
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury	1	20
Project
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam	1	50
Total

Weighting of Semester Activities on the Final Grade	2	50
Weighting of End-of-Semester Activities on the Final Grade	1	50
Total

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	14	6	84
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury	1	20	20
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms	1	35	35
Final Exam	1	38	38
		Total	225

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.		-	-	-	X	-
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	-	X	-	-
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	-	-	X	-
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	-	-	X
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	X
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	-	X	-
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		-	-	-	X	-
8	Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility.		-	-	X	-	-
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		-	-	X	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		-	-	X	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.		-	-	X	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		-	X	-	-	-