Information Package / Course Catalogue
| Course Code | : MYL506 |
| Course Type | : Area Elective |
| Couse Group | : Second Cycle (Master's Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 5 |
The purpose of the Data Science with Modern R Programming course is to help students realize data science projects using the R programming language. The course teaches students to collect data, analyze and report results using the R programming language. The course also teaches students to create functions, use data structures, and automate data science tasks in the R programming language.
The Data Science with Modern R Programming course is an educational program designed to learn the use of contemporary R programming languages. The course is designed to cover the fundamentals of data science. Students will use commands for data collection, analysis and visualization in modern R programming languages. The course content is designed to cover basic concepts that users can use to learn modern data science methods. The course will also cover basic algorithms to help understand the working logic of data science applications.
| Res. Assist. Erkam SARI |
| 1. | Learn to solve data-driven problems using appropriate data-science methods. |
| 2. | Learn to conduct up-to-date data analysis and scientific studies using the R programming language. |
| 3. | Learn to analyze data using data mining and econometric techniques. |
| 4. | Learn to make data insightful and share findings using data visualization and scheduling topics. |
| 5. | Learn how data analytics and data science can be used with social media, internet data, and other large datasets. |
| 1. | Özkan, B., & Özkan, Y. (2017). R ile Programlama, 1. Papatya Yayıncılık Eğitim, İstanbul, Türkiye. |
| 2. | Özdemir, M. U. H. L. İ. S., & Çelikbilek, Y. (2020). R ile programlama ve makine öğrenmesi. Ankara: Nobel Yayınevi. |
| Type of Assessment | Count | Percent |
|---|---|---|
| Midterm Examination | 1 | %30 |
| Final Examination | 1 | %70 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 4 | 3 | 98 |
| Midterm Examination | 1 | 10 | 1 | 11 |
| Final Examination | 1 | 20 | 1 | 21 |
| TOTAL WORKLOAD (hours) | 130 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | |
OÇ-1 | 5 | 5 | 5 | 4 | 5 |
OÇ-2 | 4 | 4 | 4 | 5 | 5 |
OÇ-3 | 5 | 5 | 5 | 5 | 4 |
OÇ-4 | 4 | 3 | 5 | 5 | 5 |
OÇ-5 | 5 | 5 | 4 | 4 | 4 |