Industrial Engineering (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:

Spring

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) Adequate knowledge in mathematics, science and industrial engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems.	2	2	2	2
2) Ability to identify, formulate, and solve complex industrial engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.	2	2	2	2
3) Ability to design a complex industrial system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose.
4) Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in industrial engineering applications; ability to use information technologies effectively.	2	2	2	2
5) Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or industrial engineering research topics.	2	2	2	3
6) Ability to work effectively within and multidisciplinary teams; individual study skills.
7) Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effectice reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8) Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously.
9) To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications.
10) Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11) Knowledge of the effects of industrial engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in industrial engineering; awareness of the legal consequences of industrial engineering solutions.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Adequate knowledge in mathematics, science and industrial engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems.	2
2)	Ability to identify, formulate, and solve complex industrial engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.	2
3)	Ability to design a complex industrial system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose.
4)	Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in industrial engineering applications; ability to use information technologies effectively.	2
5)	Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or industrial engineering research topics.	3
6)	Ability to work effectively within and multidisciplinary teams; individual study skills.
7)	Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effectice reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8)	Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously.
9)	To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications.
10)	Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11)	Knowledge of the effects of industrial engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in industrial engineering; awareness of the legal consequences of industrial engineering solutions.

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