Information Package / Course Catalogue
Data Science and Machine Learning With Modern R Programming
Course Code: MYL631
Course Type: Area Elective
Couse Group: Third Cycle (Doctorate Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 5
Objectives of the Course

The aim of the "Data Science and Machine Learning with Modern R Programming" course is to train students in the fields of data science and machine learning using the R programming language. During the course, students will learn fundamental concepts in data mining, data visualization, modelling, prediction and learning using the R programming language. The main objective of the course is to teach students the basic concepts and applications that can be used in the fields of data science and machine learning.

Course Content

The course "Data Science and Machine Learning with Modern R Programa" is designed to teach the fundamentals and applications of data science and machine learning in the R environment. The course describes the fundamentals of data science and machine learning, while also providing information on the use and application of these fields in R. Students will build a strong framework by studying sample datasets and writing code while learning key tools and concepts used in data science and machine learning in R. The course focuses specifically on data mining, rule-based learning, support vector machines, decision trees, natural language processing, and other machine learning techniques.

Name of Lecturer(s)
Res. Assist. Erkam SARI
Learning Outcomes
1.Students will understand data mining and machine learning algorithms using the R programming language.
2.Students will be able to explore the data source and perform data mining applications using R for preprocessing, modeling and visualization of results.
3.Students will understand types of modeling in R such as linear and logistic regression using datasets.
4.Students will be able to use R libraries that help data mining applications.
5.Using R, students will be able to build models such as classification, inference, and regression using machine learning in a variety of applications.
Recommended or Required Reading
1.Boehmke, B., & Greenwell, B. M. (2019). Hands-on machine learning with R. CRC press.
2.Gutierrez, D. D. (2015). Machine learning and data science: an introduction to statistical learning methods with R. Technics Publications.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction: General information about Data Science and Machine Learning, working with R and RStudio.
Week 2 - Theoretical
Data Review and Cleanup
Week 3 - Theoretical
Data Types and Data Structures
Week 4 - Theoretical
Data Analysis and Visualization
Week 5 - Theoretical
Linear Regression Models
Week 6 - Theoretical
Principal Components Analysis
Week 7 - Theoretical
Clustering Algorithms
Week 8 - Theoretical
Classification Algorithms
Week 9 - Theoretical
Deep Learning Fundamentals and Applications
Week 10 - Theoretical
Security in Machine Learning and Data Analytics
Week 11 - Theoretical
Estimation Techniques
Week 12 - Theoretical
Data Analysis Applications
Week 13 - Theoretical
The Future and Roadmap of Data Science and Machine Learning
Week 14 - Theoretical
Project Management and Reporting in Data Science
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%30
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory144398
Midterm Examination110111
Final Examination120121
TOTAL WORKLOAD (hours)130
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
OÇ-1
5
5
4
4
5
OÇ-2
5
4
4
5
5
OÇ-3
5
4
5
5
5
OÇ-4
5
5
4
5
5
OÇ-5
4
4
5
5
5
Adnan Menderes University - Information Package / Course Catalogue
2026