UNI280 Data Analysis with R Istinye UniversityDegree Programs Management Information Systems (English)General Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
Management Information Systems (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
5
Language of instruction: English
Course Condition:
Does the Course Require Work Experience?: No
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) It has a wide range of interdisciplinary approaches to management information systems, primarily business and computer engineering.
2) Comprehends the management information systems in terms of technical, organizational and managerial aspects and uses the current programming language by knowing the logic of programming.
3) Uses different information technologies and systems for understanding and solving various business problems.
4) Interpret the data, concepts and ideas in the field of management information systems with scientific and technological methods.
5) Analyze the needs for an information system and analyze the processes of analysis, design and implementation of the database.
6) Gains technical and managerial contributions to IT projects and takes responsibility.
7) Solve complex business and informatics problems by using various statistical techniques and numerical methods and make analyzes using statistical programs effectively.
8) Uses a foreign language at the B1 General Level in terms of European Language Portfolio criteria according to the level of education.
9) Develops teamwork, negotiation, leadership and entrepreneurship skills.
10) Has universal ethical values, social responsibility awareness and sufficient legal knowledge.
11) Develops positive attitudes related to lifelong learning and identifies individual learning needs and carries out studies to correct them.
12) Students will be able to communicate their ideas and solutions both written and orally, and present and publish them on both national and international platforms.
13) It uses 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) It has a wide range of interdisciplinary approaches to management information systems, primarily business and computer engineering. 3
2) Comprehends the management information systems in terms of technical, organizational and managerial aspects and uses the current programming language by knowing the logic of programming. 3
3) Uses different information technologies and systems for understanding and solving various business problems. 2
4) Interpret the data, concepts and ideas in the field of management information systems with scientific and technological methods. 3
5) Analyze the needs for an information system and analyze the processes of analysis, design and implementation of the database. 3
6) Gains technical and managerial contributions to IT projects and takes responsibility. 2
7) Solve complex business and informatics problems by using various statistical techniques and numerical methods and make analyzes using statistical programs effectively. 3
8) Uses a foreign language at the B1 General Level in terms of European Language Portfolio criteria according to the level of education. 3
9) Develops teamwork, negotiation, leadership and entrepreneurship skills. 3
10) Has universal ethical values, social responsibility awareness and sufficient legal knowledge. 3
11) Develops positive attitudes related to lifelong learning and identifies individual learning needs and carries out studies to correct them. 2
12) Students will be able to communicate their ideas and solutions both written and orally, and present and publish them on both national and international platforms. 3
13) It uses information and communication technologies together with computer software at the advanced level of European Computer Driving License required by the field. 3

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