Physical Therapy and Rehabilitation
Course Details
KTO KARATAY UNIVERSITY
School of Health Sciences
Programme of Physical Therapy and Rehabilitation
Course Details
School of Health Sciences
Programme of Physical Therapy and Rehabilitation
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 99901006 | Data Science | 1 | Autumn | 1 | 2+0+0 | 3 | 3 |
| 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) | - |
| Instructor Assistant(s) | - |
Course Content
A general introduction to the topics of Data Science. ,Basic data models, relational models of entities, relational models and SQL databases and related data models. Sources and types of big data, frequent analysis. Data literacy , Data Analysis and preprocessing , Data science project management and Student presentations on research topics and techniques . ,Recommendation systems; includes topics.
Objectives of the Course
It lays out the fundamentals of data science, which is rapidly evolving in the 21st century and is very popular with researchers and practitioners alike. The course transfers the basic skills that a data scientist should have to the student and enables the student to apply them to different fields.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | A general introduction to Data Science topics |
| 2 | Basic data models, relational models of entities, relational models |
| 3 | SQL to NoSQL, non-relational databases and related data models |
| 4 | Data Literacy |
| 5 | Sources and types of big data, frequent pattern analysis. |
| 6 | Presentations of students about research topics and techniques. |
| 7 | Presentations of students about research topics and techniques. |
| 8 | Presentations of students about research topics and techniques. |
| 9 | Data Analysis |
| 10 | Data Preprocessing |
| 11 | Data Science Project Management |
| 12 | Presentations of students about research topics and techniques. |
| 13 | Presentations of students about research topics and techniques. |
| 14 | Presentations of students about research topics and techniques. |
Textbook or Material
| Resources | Araştırma Makaleleri |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Practice | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Seminar | - | - |
| Quiz | - | - |
| Midterms | - | - |
| Final Exam | - | - |
| Total | 0 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 0 | 0 | 0 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 0 | 0 | 0 |
| Midterms | 0 | 0 | 0 |
| 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 | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 0 | ||
| Total Work Load / 30 | 0 | ||
| Course ECTS Credits: | 0 | ||