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Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| COMPUTER SCIENCES PR. | |
| Code | BBZ312 |
| Name | Data Analysis with R |
| Term | 2025-2026 Academic Year |
| Semester | 6. Semester |
| Duration (T+A) | 3-1 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3.5 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Label | FE Field Education Courses E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. GÜZİN YÜKSEL |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The goal of this course is to teach students the basics of the R programming language and how to use R for data analysis.
Course Content
In this course, data structures and data entry, various mathematical and statistical operations, graph drawings, random number generation, solving problems with simulation, writing functions for various methods, cross tables, hypothesis testing are covered using the R programming language.
Course Precondition
It is not available.
Resources
İstatistikte R ile programlama, 2014, Necmi Gürsakal, Dora Yayıncılık 2.
Notes
VAKFI YAYINCILIK - AKADEMİK KİTAPLAR. A Tiny Handbook of R, Mike Allerhand, 2011, Springer-Verlag.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Installs the R program on their personal computers. |
| LO02 | Generates random numbers with R program. |
| LO03 | Performs applications related to probability distributions. |
| LO04 | Uses the R programming language in data analysis. |
| LO05 | Çeşitli olasılık problemlerinin R'da simulasyon ile çözümünü elde eder. |
| LO06 | Defines functions for hypothesis testing in R. |
| LO07 | Generates code for ANOVA and t-tests. |
| LO08 | Implements linear regression analysis in R. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Gain comprehensive knowledge of fundamental concepts, algorithms, and data structures in Computer Science. | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Learn essential computer topics such as software development, programming languages, and database management | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Understand advanced computer fields like data science, artificial intelligence, and machine learning. | 3 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Acquire knowledge of topics like computer networks, cybersecurity, and database design. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Develop skills in designing, implementing, and analyzing algorithms | 3 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Gain proficiency in using various programming languages effectively | 4 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Learn skills in data analysis, database management, and processing large datasets. | 3 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Acquire practical experience through working on software development projects. | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Strengthen teamwork and communication skills. | |
| PLO10 | Yetkinlikler - Alana Özgü Yetkinlik | Foster a mindset open to technological innovations. | |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Encourage the capacity for continuous learning and self-improvement. | 3 |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Enhance the ability to solve complex problems | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Installation of R software and introduction to programming | Reading from sources | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma, Soru-Cevap |
| 2 | Data structures and data entry in R | Reading from sources | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 3 | Various mathematical and statistical operations using vectors, matrices and data frames | Reading from sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Graphic drawings | Reading from sources | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma, Bireysel Çalışma |
| 5 | Random number generation from various probability distributions | Reading from sources | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma, Alıştırma ve Uygulama |
| 6 | Solving various probability problems with simulation in R | Reading from sources | Öğretim Yöntemleri: Soru-Cevap, Alıştırma ve Uygulama |
| 7 | Writing a function in R | Reading from sources | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 8 | Mid-Term Exam | Reading lecture notes and resources. | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Cross Tables | Reading from sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
| 10 | Hypothesis testing for one and two samples | Reading from sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | Write functions for hypothesis testing for one and two samples. | Reading from sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Tartışma |
| 12 | One-way analysis of variance and writing functions | Reading from sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Gösterip Yaptırma |
| 13 | Linear regression analysis and writing the function | Reading from sources | Öğretim Yöntemleri: Anlatım, Tartışma, Alıştırma ve Uygulama |
| 14 | Comparison of t-test and Mann-Whitney test in independent groups. | Reading from sources | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma, Soru-Cevap |
| 15 | General Review | Reviewing lecture notes | Öğretim Yöntemleri: Soru-Cevap, Tartışma |
| 16 | Term Exams | Reading lecture notes and resources. | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Reading lecture notes and resources. | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 8 | 8 |
| Final Exam | 1 | 16 | 16 |
| Total Workload (Hour) | 122 | ||
| Total Workload / 25 (h) | 4,88 | ||
| ECTS | 5 ECTS | ||