Islamic Economics and Finance
Course Details
KTO KARATAY UNIVERSITY
İktisadi, İdari ve Sosyal Bilimler Fakültesi
Programme of Islamic Economics and Finance
Course Details
İktisadi, İdari ve Sosyal Bilimler Fakültesi
Programme of Islamic Economics and Finance
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 4781105 | Python and R Applications | 3 | Spring | 6 | 3+0+0 | 4 | 4 |
| Course Type | Elective |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | Asst. Prof. Selahattin BEKTAŞ |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. Selahattin BEKTAŞ | C-Z10 | [email protected] | 7339 |
Course Content
Bu ders, Python ve R programlama dillerinin veri analizi, istatistiksel modelleme, veri görselleştirme ve makine öğrenmesi uygulamalarıyla gerçek dünya projelerinde nasıl kullanılacağını kapsamaktadır.
Objectives of the Course
The aim of this course is to teach how to use Python and R programming languages in data analysis and statistical modeling. Students will learn how to process data, visualize results, and implement statistical analyses and machine learning algorithms using Python and R, understanding the strengths of both languages. Additionally, they will develop the skills to create projects in both languages.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | X |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P1 | İslam İktisadı ve Finans alanında kuramsal ve uygulamalı bilgilere sahip olma, sahip olduğu bilgileri kullanabilme | 4 |
| P2 | İslam İktisadı ve Finans alanında edindiği bilgi, beceri ve yetkinlikleri kullanarak meseleleri tanımlama, veri toplama, değerlendirme, analiz etme, yorumlama ve çözüm önerisi geliştirebilme | 3 |
| P3 | İslam İktisadı ve Finans alanıyla ilgili farklı bilgi kaynaklarına erişip sayısal analiz ve araştırma yapabilme | 4 |
| P4 | Disiplin içi, çok disiplinli veya çok kültürlü gruplarda ve bireysel çalışabilme | 3 |
| P5 | Ahlaki değerler ve mesleki sorumluluk bilinci ile hareket edebilme | 3 |
| P6 | Alan uygulamalarının, evrensel ve toplumsal etkileri ile hukuki sonuçlarını bilme | 3 |
| P7 | Öğrenim dilinde yazılı ve sözlü iletişim kurabilme, en az bir yabancı dil bilgisine sahip olabilme | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Critically evaluates the results of numerical analyses. | P.3.5 | 1 |
| O2 | Shares research results in the form of scientific articles, reports, and presentations. | P.3.6 | 1 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Introduction and Programming Fundamentals: Introduction to Python and R |
| 2 | Data Types and Variables |
| 3 | Control Structures and Loops |
| 4 | Functions and Modules |
| 5 | Data Reading and Writing: File Operations |
| 6 | Data Analysis and Visualization |
| 7 | Statistical Analysis and Data Manipulation |
| 8 | Introduction to Data Science Projects |
| 9 | Fundamentals of Machine Learning |
| 10 | Regression and Classification Models |
| 11 | Time Series Analysis |
| 12 | Big Data and Cloud Computing |
| 13 | Project Management and Applications |
| 14 | Project Management and Applications |
Textbook or Material
| Resources | Python Ve R Uygulamaları İle Stokastik Süreçler Teori – Uygulama |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 2 | 28 |
| Midterms | 1 | 20 | 20 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 30 | 30 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 120 | ||
| Total Work Load / 30 | 4 | ||
| Course ECTS Credits: | 4 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P3 |
|---|---|---|
| O1 | Critically evaluates the results of numerical analyses. | 5 |
| O2 | Shares research results in the form of scientific articles, reports, and presentations. | 4 |