Mathematics (English) Preview
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code:

ISE026

Course Name:

Applied Statistics and Data Analysis

Semester:

Fall

Course Credits:

ECTS

Language of instruction:

English

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Departmental Elective

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator:

Doç. Dr. SALİHA KARADAYI USTA

Course Lecturer(s):

Dr. Öğr. Üy. EMRE ÇAKMAK

Course Assistants:

Course Objective and Content

Course Objectives:	The course aims to provide students with the ability to solve problems related to data analysis that arise in various fields in real life, by using statistical techniques, as well as teaching basic statistical concepts.
Course Content:	Introduction to Scientific Evidence and Statistics, Measures of central tendency and the normal distribution, Probability, Discrete random variables and probability distributions, Estimation of mean and standard deviation and the normal distribution, Hypothesis testing for one or two population means, Student t-test, Analysis of Variance and multiple comparison tests, Simple linear regression

Learning Outcomes

The students who have succeeded in this course;
1) Should know basic statistical concepts such as mean, variable, standard deviation.
2) Must have sufficient knowledge about basic probability issues such as random variables and probability distributions.
3) Should be able to use statistical methods such as hypothesis testing, analysis of variance and linear regression.
4) Should be able to apply statistical methods such as hypothesis testing, analysis of variance and linear regression to data in different fields.

Course Flow Plan

Week	Subject	Related Preparation
1)	Introduction to Scientific Evidence and Statistics
2)	Measures of central tendency and the normal distribution
3)	Probability
4)	Discrete random variables and probability distributions
5)	Discrete random variables and probability distributions
6)	Estimation of mean and standard deviation and the normal distribution
7)	Hypothesis testing for one or two population means, Student t-test
8)	Midterm exam
9)	Hypothesis testing for one or two population means, Student t-test
10)	Hypothesis testing for small sample sizes and multinomial experiments, Fisher’s exact test
11)	Analysis of Variance and multiple comparison tests
12)	Analysis of Variance and multiple comparison tests
13)	Simple linear regression
14)	Simple linear regression

Sources

Course Notes / Textbooks:	Mann, P. S. (2007). Introductory statistics. John Wiley & Sons.
References:	Devore, J. L., Farnum, N. R., & Doi, J. A. (2013). Applied statistics for engineers and scientists. Cengage Learning.

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4
Program Outcomes
1) Have the knowledge of the scope, history, applications, problems, methods of mathematics and knowledge that will be beneficial to humanity as both scientific and intellectual discipline.	2	2	2	2
2) Have the ability to establish a relationship between mathematics and other disciplines and develop mathematical models for interdisciplinary problems.	2	2	2	2
3) Have the ability to define, formulate and analyze real life problems with statistical and mathematical techniques.
4) Have the ability to think analytically and use the time effectively in the process of deduction.	2	2	2	2
5) Have the ability to search the literature, understand and interpret scientific articles.	2	2	2	3
6) Have the knowledge of basic software to be able to work in the related fields of computer science and have the ability to use information technologies at an advanced level of the European Computer Driving License.
7) Have the ability to work efficiently in interdisciplinary teams.
8) Have the ability to communicate effectively in oral and written form, write effective reports and comprehend the written reports, make effective presentations.
9) Have the consciousness of professional and ethical responsibility and acting ethically; have the knowledge about academic standards.
10) Have the ability to use a foreign language at least at B1 level in terms of European Language Portfolio criteria.
11) Are aware of the necessity of lifelong learning; have the ability to access information, to follow developments in science and technology and to constantly renew themselves.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Have the knowledge of the scope, history, applications, problems, methods of mathematics and knowledge that will be beneficial to humanity as both scientific and intellectual discipline.
2)	Have the ability to establish a relationship between mathematics and other disciplines and develop mathematical models for interdisciplinary problems.
3)	Have the ability to define, formulate and analyze real life problems with statistical and mathematical techniques.
4)	Have the ability to think analytically and use the time effectively in the process of deduction.
5)	Have the ability to search the literature, understand and interpret scientific articles.
6)	Have the knowledge of basic software to be able to work in the related fields of computer science and have the ability to use information technologies at an advanced level of the European Computer Driving License.
7)	Have the ability to work efficiently in interdisciplinary teams.
8)	Have the ability to communicate effectively in oral and written form, write effective reports and comprehend the written reports, make effective presentations.
9)	Have the consciousness of professional and ethical responsibility and acting ethically; have the knowledge about academic standards.
10)	Have the ability to use a foreign language at least at B1 level in terms of European Language Portfolio criteria.
11)	Are aware of the necessity of lifelong learning; have the ability to access information, to follow developments in science and technology and to constantly renew themselves.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	4	% 20
Midterms	1	% 30
Final	1	% 50
total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Preparation for the Activity	Spent for the Activity Itself	Completing the Activity Requirements	Workload
Course Hours	13	0	3		39
Study Hours Out of Class	13	0	1		13
Homework Assignments	4	0	10		40
Midterms	1	8	2		10
Final	1	18	2		20
Total Workload					122