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:

UNI280

Course Name:

Data Analysis with R

Semester:

Fall

Course Credits:

ECTS

Language of instruction:

English

Course Condition:

Does the Course Require Work Experience?:

Type of course:

University Elective

Course Level:

Bachelor

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

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

E-Learning

Course Coordinator:

Öğr. Gör. AYŞEGÜL ÇALIŞKAN İŞCAN

Course Lecturer(s):

Dr. Ayşegül Çalışkan İşcan

Course Assistants:

Course Objective and Content

Course Objectives:	This course aims to teach the R programming language at a basic level.
Course Content:	This course includes basic elements of R programming languages.

Learning Outcomes

The students who have succeeded in this course;
1) Have knowledge about R programming language
2) Learns R programming language at a basic level.
3) Can analyze any data by using R language.
4) Can understand and manipulate any R code.
5) Can make statistical analysis by using R language.

Course Flow Plan

Week	Subject	Related Preparation
1)	Course overview
2)	R Arithmetic, Atomic Data Types
3)	Variables, Vectors
4)	Matrices
5)	Lists, Data Frames
6)	Factors, Reading and Writing Data
7)	Exercises, Lesson Repetition
8)	Mid-term Week
9)	Control flow, functions
10)	Exploring and Preparing Data
11)	Working with text data
12)	Preparing Numeric Data, Dealing with Dates
13)	Merging data, Frequency tables
14)	Plotting in Base R, plotting with ggplot2

Sources

Course Notes / Textbooks:	1. Mark Gardener - Beginning R_ The Statistical Programming Language-Wrox 2. Tony Fischetti - Data Analysis with R_ Load, wrangle, and analyze your data using the world's most powerful statistical programming language-Packt Publishing (2015)
References:	1. Mark Gardener - Beginning R_ The Statistical Programming Language-Wrox 2. Tony Fischetti - Data Analysis with R_ Load, wrangle, and analyze your data using the world's most powerful statistical programming language-Packt Publishing (2015)

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4	5
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) Ability to identify, formulate, and solve complex industrial engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.
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.
5) Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or industrial engineering research topics.
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)	Ability to identify, formulate, and solve complex industrial engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.
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.
5)	Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or industrial engineering research topics.
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
Application	13	% 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	1	3	1	65
Homework Assignments	13	1	1		26
Midterms	1	14	1	1	16
Final	1	28	1	1	30
Total Workload					137