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Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| STATISTICS PR. | |
| Code | ISB205 |
| Name | Introduction to Statistical Programming with R |
| Term | 2025-2026 Academic Year |
| Semester | 3. Semester |
| Duration (T+A) | 2-0 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 2 National Credit |
| Teaching Language | Türkçe |
| Level | Lisans Dersi |
| Type | Normal |
| Label | VK Vocational Knowledge Courses E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. ALİ İHSAN GENÇ |
| Course Instructor |
Dr. Öğr. Üyesi İsmet BİRBİÇER
(Güz)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
The aim is to give an insight for programming with R program and to gain the ability of performing explatory data analysis.
Course Content
The scope of this course is basics of R program and programming, data manipulation, defining function, data visualisation, explatory data analysis.
Course Precondition
None.
Resources
Braun, W. J., & Murdoch, D. J. (2021). A first course in statistical programming with R. Cambridge University Press.
Notes
Braun, W. J., & Murdoch, D. J. (2021). A first course in statistical programming with R. Cambridge University Press.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains the basics of R language. |
| LO02 | Applies data manipulation techniques. |
| LO03 | Write codes to define functions. |
| LO04 | Performs descriptive data analysis. |
| LO05 | Uses data visualisation methods. |
| LO06 | Performs explatory data analysis. |
| LO07 | Uses R like a calculator. |
| LO08 | Finds approximate solutions to some optimization prolems. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explain the essence fundamentals and concepts in the field of Statistics | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Emphasize the importance of Statistics in life | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Define basic principles and concepts in the field of Law and Economics | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Produce numeric and statistical solutions in order to overcome the problems | |
| PLO05 | Bilgi - Kuramsal, Olgusal | Use proper methods and techniques to gather and/or to arrange the data | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Utilize computer programs and builds models, solves problems, does analyses and comments about problems concerning randomization | 2 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Apply the statistical analyze methods | 2 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Make statistical inference (estimation, hypothesis tests etc.) | |
| PLO09 | Bilgi - Kuramsal, Olgusal | Generate solutions for the problems in other disciplines by using statistical techniques and gain insight | 3 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Discover the visual, database and web programming techniques and posses the ability of writing programs | |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Distinguish the difference between the statistical methods | 1 |
| PLO12 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Make oral and visual presentation for the results of statistical methods | 4 |
| PLO13 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Have capability on effective and productive work in a group and individually | 1 |
| PLO14 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs | |
| PLO15 | Yetkinlikler - Öğrenme Yetkinliği | Develop scientific and ethical values in the fields of statistics-and scientific data collection | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to R program and menus | Reading the source book and making applications | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 2 | Basic computing with R | Reading the source book and making applications | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 3 | Data vectors | Reading the source book and making applications | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Vector operations | Reading the source book and making applications | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 5 | Constructing data frames, lists and arrays | Reading the source book and making applications | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 6 | Manipulation with external files | Reading the source book and making applications | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 7 | Matrix operations | Reading the source book and making applications | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Review the topics | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | One dimensional plots | Reading the source book and making applications | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 10 | Conditionals and loops | Reading the source book and making applications | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | Defining functions with R | Reading the source book and making applications | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 12 | Making frequency tables and congintency tables | Reading the source book and making applications | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Basic statistical functions in R | Reading the source book and making applications | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 14 | Analytical descriptive statistics | Reading the source book and making applications | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 15 | Graphical descriptive data analysis | Reading the source book and making applications | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 16 | Term Exams | Review the topics | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Review the topics | Ölçme Yöntemleri: Yazılı Sınav |
Assessment (Exam) Methods and Criteria
Current term shares have not yet been determined. Shares of the previous term are shown.
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 2 | 28 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 2 | 28 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 6 | 6 |
| Final Exam | 1 | 16 | 16 |
| Total Workload (Hour) | 78 | ||
| Total Workload / 25 (h) | 3,12 | ||
| ECTS | 3 ECTS | ||