Course Description
Course | Code | Semester | T+P (Hour) | Credit | ECTS |
---|---|---|---|---|---|
INTRODUCTION to DATA SCIENCE and MACHINE LEARNING | - | Fall Semester | 3+0 | 3 | 8 |
Course Program |
Prerequisites Courses | |
Recommended Elective Courses |
Language of Course | English |
Course Level | Second Cycle (Master's Degree) |
Course Type | Elective |
Course Coordinator | Prof.Dr. Abdulbari BENER |
Name of Lecturer(s) | Assist.Prof. Kıvanç KÖK |
Assistant(s) | |
Aim | To be able to apply and evaluate machine learning techniques used in the field of health. |
Course Content | This course contains; Introduction to Data Science and Machine Learning and Basic Concepts,Machine Learning,Data exploration and visualization,Variable selection and data transformation,Clustering Techniques,Cluster Algorithms Applications,Classification Methods-Decision Trees,Decision Tree Algorithms Applications,Classification Algorithms,Ensemble Learning Techniques,Applications of Ensemble Learning Techniques,Association Rules,Student Presentations,Student Presentations. |
Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
2) Will be able to apply common machine learning methods. | 12, 14, 6, 9 | E |
1) Will be able to explain and internalize the basic concepts and processes of data mining. | 12, 14, 6, 9 | A, E |
4) Will be able to apply Machine Learning methods in a package program. | 14, 16, 2, 6, 9 | E |
3 ) Will be able to apply the appropriate machine learning method to the existing problems in the field of health. | 12, 14, 2, 6 | E |
Teaching Methods: | 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 6: Experiential Learning, 9: Lecture Method |
Assessment Methods: | A: Traditional Written Exam, E: Homework |
Course Outline
Order | Subjects | Preliminary Work |
---|---|---|
1 | Introduction to Data Science and Machine Learning and Basic Concepts | Related chapters in textbooks |
2 | Machine Learning | Related chapters in textbooks |
3 | Data exploration and visualization | Related chapters in textbooks |
4 | Variable selection and data transformation | Related chapters in textbooks |
5 | Clustering Techniques | Related chapters in textbooks |
6 | Cluster Algorithms Applications | Related chapters in textbooks |
7 | Classification Methods-Decision Trees | Related chapters in textbooks |
8 | Decision Tree Algorithms Applications | Related chapters in textbooks |
9 | Classification Algorithms | Related chapters in textbooks |
10 | Ensemble Learning Techniques | Related chapters in textbooks |
11 | Applications of Ensemble Learning Techniques | Related chapters in textbooks |
12 | Association Rules | Related chapters in textbooks |
13 | Student Presentations | Lecture Notes |
14 | Student Presentations | Lecture Notes |
Resources |
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications | |||||||
No | Program Qualification | Contribution Level | |||||
1 | 2 | 3 | 4 | 5 | |||
1 | Can use advanced theoretical and applied knowledge gained in the fields of theoretical and applied biostatistics. | X | |||||
2 | Can use the knowledge of basic probability and statistics theories and applications at the level of expertise. | X | |||||
3 | They have knowledge of all kinds of research design in the field of health | X | |||||
4 | Can design, construct and propose solutions for research in the field of health. | X | |||||
5 | Can identify and analyze problems in health research and produce solutions based on scientific methods | X | |||||
6 | Conducts scientific clinical descriptive or analytical research on priority issues related to the field. | X | |||||
7 | Evaluate and explain the information about the field of biostatistics with a critical approach. | X | |||||
8 | Observes and teaches social, scientific, and ethical values in the stages of data collection, recording, interpretation, and reporting related to the field of biostatistics. | X | |||||
9 | To be familiar with the software commonly used in the fields of biostatistics and to be able to use at least one effectively | X | |||||
10 | Conducts studies in the field of biostatistics independently or as a team. | X | |||||
11 | Maintains work in the field of biostatistics individually or as a team, can participate in the decision-making process, and make and finalize the necessary planning by using time effectively. | X | |||||
12 | Ensure the continuity of her professional development by using the biostatistics field and lifelong learning principles. | X | |||||
13 | Publishes a scientific article in a national and international journal or presents it at a scientific meeting. | X | |||||
14 | Take part in research, projects and activities in collaboration with other disciplines in the field of health. | X | |||||
15 | A sensitive individual, they can use their knowledge for the benefit of society and have sufficient awareness about quality management, occupational safety, and environment in all processes. | X | |||||
16 | Can use the knowledge and problem-solving skills synthesized in the field of biostatistics by considering ethical principles in health research. | X | |||||
17 | It can be found in national and international policy studies in the field of biostatistics and education. | X |
Assessment Methods
Contribution Level | Absolute Evaluation | |
Rate of Midterm Exam to Success | 50 | |
Rate of Final Exam to Success | 50 | |
Total | 100 |
ECTS / Workload Table | ||||||
Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
Course Hours | 0 | 0 | 0 | |||
Guided Problem Solving | 0 | 0 | 0 | |||
Resolution of Homework Problems and Submission as a Report | 0 | 0 | 0 | |||
Term Project | 0 | 0 | 0 | |||
Presentation of Project / Seminar | 0 | 0 | 0 | |||
Quiz | 0 | 0 | 0 | |||
Midterm Exam | 0 | 0 | 0 | |||
General Exam | 0 | 0 | 0 | |||
Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
Total Workload(Hour) | 0 | |||||
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(0/30) | 0 | |||||
ECTS of the course: 30 hours of work is counted as 1 ECTS credit. |
Detail Informations of the Course
Course Description
Course | Code | Semester | T+P (Hour) | Credit | ECTS |
---|---|---|---|---|---|
INTRODUCTION to DATA SCIENCE and MACHINE LEARNING | - | Fall Semester | 3+0 | 3 | 8 |
Course Program |
Prerequisites Courses | |
Recommended Elective Courses |
Language of Course | English |
Course Level | Second Cycle (Master's Degree) |
Course Type | Elective |
Course Coordinator | Prof.Dr. Abdulbari BENER |
Name of Lecturer(s) | Assist.Prof. Kıvanç KÖK |
Assistant(s) | |
Aim | To be able to apply and evaluate machine learning techniques used in the field of health. |
Course Content | This course contains; Introduction to Data Science and Machine Learning and Basic Concepts,Machine Learning,Data exploration and visualization,Variable selection and data transformation,Clustering Techniques,Cluster Algorithms Applications,Classification Methods-Decision Trees,Decision Tree Algorithms Applications,Classification Algorithms,Ensemble Learning Techniques,Applications of Ensemble Learning Techniques,Association Rules,Student Presentations,Student Presentations. |
Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
2) Will be able to apply common machine learning methods. | 12, 14, 6, 9 | E |
1) Will be able to explain and internalize the basic concepts and processes of data mining. | 12, 14, 6, 9 | A, E |
4) Will be able to apply Machine Learning methods in a package program. | 14, 16, 2, 6, 9 | E |
3 ) Will be able to apply the appropriate machine learning method to the existing problems in the field of health. | 12, 14, 2, 6 | E |
Teaching Methods: | 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 6: Experiential Learning, 9: Lecture Method |
Assessment Methods: | A: Traditional Written Exam, E: Homework |
Course Outline
Order | Subjects | Preliminary Work |
---|---|---|
1 | Introduction to Data Science and Machine Learning and Basic Concepts | Related chapters in textbooks |
2 | Machine Learning | Related chapters in textbooks |
3 | Data exploration and visualization | Related chapters in textbooks |
4 | Variable selection and data transformation | Related chapters in textbooks |
5 | Clustering Techniques | Related chapters in textbooks |
6 | Cluster Algorithms Applications | Related chapters in textbooks |
7 | Classification Methods-Decision Trees | Related chapters in textbooks |
8 | Decision Tree Algorithms Applications | Related chapters in textbooks |
9 | Classification Algorithms | Related chapters in textbooks |
10 | Ensemble Learning Techniques | Related chapters in textbooks |
11 | Applications of Ensemble Learning Techniques | Related chapters in textbooks |
12 | Association Rules | Related chapters in textbooks |
13 | Student Presentations | Lecture Notes |
14 | Student Presentations | Lecture Notes |
Resources |
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications | |||||||
No | Program Qualification | Contribution Level | |||||
1 | 2 | 3 | 4 | 5 | |||
1 | Can use advanced theoretical and applied knowledge gained in the fields of theoretical and applied biostatistics. | X | |||||
2 | Can use the knowledge of basic probability and statistics theories and applications at the level of expertise. | X | |||||
3 | They have knowledge of all kinds of research design in the field of health | X | |||||
4 | Can design, construct and propose solutions for research in the field of health. | X | |||||
5 | Can identify and analyze problems in health research and produce solutions based on scientific methods | X | |||||
6 | Conducts scientific clinical descriptive or analytical research on priority issues related to the field. | X | |||||
7 | Evaluate and explain the information about the field of biostatistics with a critical approach. | X | |||||
8 | Observes and teaches social, scientific, and ethical values in the stages of data collection, recording, interpretation, and reporting related to the field of biostatistics. | X | |||||
9 | To be familiar with the software commonly used in the fields of biostatistics and to be able to use at least one effectively | X | |||||
10 | Conducts studies in the field of biostatistics independently or as a team. | X | |||||
11 | Maintains work in the field of biostatistics individually or as a team, can participate in the decision-making process, and make and finalize the necessary planning by using time effectively. | X | |||||
12 | Ensure the continuity of her professional development by using the biostatistics field and lifelong learning principles. | X | |||||
13 | Publishes a scientific article in a national and international journal or presents it at a scientific meeting. | X | |||||
14 | Take part in research, projects and activities in collaboration with other disciplines in the field of health. | X | |||||
15 | A sensitive individual, they can use their knowledge for the benefit of society and have sufficient awareness about quality management, occupational safety, and environment in all processes. | X | |||||
16 | Can use the knowledge and problem-solving skills synthesized in the field of biostatistics by considering ethical principles in health research. | X | |||||
17 | It can be found in national and international policy studies in the field of biostatistics and education. | X |
Assessment Methods
Contribution Level | Absolute Evaluation | |
Rate of Midterm Exam to Success | 50 | |
Rate of Final Exam to Success | 50 | |
Total | 100 |