Business Administration (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:

Spring

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) Using other social sciences and mathematics, they have a broad and interdisciplinary perspective on business and management sciences.
1) Using other social sciences and mathematics, they have a broad and interdisciplinary perspective on business and management sciences.
2) They have knowledge and skills about different functions and interactions of the enterprise.
3) They can use different theoretical approaches to understanding and solving various business problems.
4) Being aware of the needs of society, they use business knowledge to meet these needs.
5) They have knowledge depthly about current problems of Turkey and Global Business World’s
6) They can determine the objectives of the institution in which they are involved, taking into account the market needs and economic conditions.
7) They can solve complex business problems by using various statistical techniques and numerical methods and makes analysis by using statistical programs effectively.
8) They can use a foreign language at least B1 General Level in terms of European Language Portfolio criteria according to the education level of a foreign language.
9) They can develops teamwork, negotiation, leadership and entrepreneurship skills.
10) They have the knowledge of universal ethical values, social responsibility awareness and sufficient level of labor law.
11) They can identify the individual learning needs and carries out studies to correct them by developing positive attitudes about lifelong learning.
12) They can express their ideas and solutions both written and orally, and if required they can present and publish them on both national and international platforms.
13) They use information and communication technologies together with computer software at the advanced level of European Computer Driving License required by the field.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Using other social sciences and mathematics, they have a broad and interdisciplinary perspective on business and management sciences.
1)	Using other social sciences and mathematics, they have a broad and interdisciplinary perspective on business and management sciences.
2)	They have knowledge and skills about different functions and interactions of the enterprise.
3)	They can use different theoretical approaches to understanding and solving various business problems.
4)	Being aware of the needs of society, they use business knowledge to meet these needs.
5)	They have knowledge depthly about current problems of Turkey and Global Business World’s
6)	They can determine the objectives of the institution in which they are involved, taking into account the market needs and economic conditions.
7)	They can solve complex business problems by using various statistical techniques and numerical methods and makes analysis by using statistical programs effectively.
8)	They can use a foreign language at least B1 General Level in terms of European Language Portfolio criteria according to the education level of a foreign language.
9)	They can develops teamwork, negotiation, leadership and entrepreneurship skills.
10)	They have the knowledge of universal ethical values, social responsibility awareness and sufficient level of labor law.
11)	They can identify the individual learning needs and carries out studies to correct them by developing positive attitudes about lifelong learning.
12)	They can express their ideas and solutions both written and orally, and if required they can present and publish them on both national and international platforms.
13)	They use information and communication technologies together with computer software at the advanced level of European Computer Driving License required by the field.

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