Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|
| DATA SCIENCE | BPR2214995 | Spring Semester | 3+0 | 3 | 5 |
| Prerequisites Courses | |
| Recommended Elective Courses | |
| Language of Course | Turkish |
| Course Level | Short Cycle (Associate's Degree) |
| Course Type | Elective |
| Course Coordinator | Lect. Beyza KOYULMUŞ |
| Name of Lecturer(s) | Lect. Beyza KOYULMUŞ |
| Assistant(s) | |
| Aim | Aims to teach all the methods, processes, algorithms and software applied to extract information from various data. |
| Course Content | This course contains; Introduction to Data Science,Basic concepts of Data Science,Application development stages in data science,Examination of tools used in data science,Creating a data set,Exploratory data analysis operations: review and preparation of the data set,Exploratory data analysis operations: attribute addition and extraction,Exploratory data analysis operations: data filtering, missing data completion,Exploratory data analysis procedures: acquiring basic statistical knowledge,Exploratory data analysis operations: outlier detection,Exploratory data analysis processes: data visualization,Use of machine learning algorithms (classification and clustering),Project Development,Project Development. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Develops a data science application on a data set | 2, 6, 9 | A, E, F |
| Defines the relationship between Data Science and Big Data. | 2, 23, 9 | A, E, F |
| Explains the basic concepts of data science. | 2, 6, 9 | A, E, F |
| Analyzes data sets. | 2, 6, 9 | A, E, F |
| Gains the ability to use data science and modeling tools | 2, 6, 9 | A, E, F |
| Learns how to extract useful information from data | 2, 9 | A, E, F |
| Teaching Methods: | 2: Project Based Learning Model, 23: Concept Map Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|
| 1 | Introduction to Data Science | |
| 2 | Basic concepts of Data Science | |
| 3 | Application development stages in data science | |
| 4 | Examination of tools used in data science | |
| 5 | Creating a data set | |
| 6 | Exploratory data analysis operations: review and preparation of the data set | |
| 7 | Exploratory data analysis operations: attribute addition and extraction | |
| 8 | Exploratory data analysis operations: data filtering, missing data completion | |
| 9 | Exploratory data analysis procedures: acquiring basic statistical knowledge | |
| 10 | Exploratory data analysis operations: outlier detection | |
| 11 | Exploratory data analysis processes: data visualization | |
| 12 | Use of machine learning algorithms (classification and clustering) | |
| 13 | Project Development | |
| 14 | Project Development | |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications |
| No | Program Qualification | Contribution Level |
| 1 | 2 | 3 | 4 | 5 |
| 1 | Has the background in algorithms, programming, and application development in software engineering projects; and has the ability to use them together in business. | | | X | | |
| 2 | Chooses and uses the proper solution methods and special techniques for programming purpose. | | | X | | |
| 3 | Uses modern techniques and tools for programming applications. | | | X | | |
| 4 | Works effectively individually and in teams. | | | X | | |
| 5 | Implements and follows test cases of developed software and applications. | X | | | | |
| 6 | Has the awareness in workplace practices, worker health, environmental and workplace safety, professional and ethical responsibility, and legal issues about programming practices. | X | | | | |
| 7 | Reaches information, and surveys resources for this purpose. | | | | | X |
| 8 | Aware of the necessity of life-long learning; follows technological advances and renews him/herself. | | | X | | |
| 9 | Communicates, oral and written, effectively using modern tools. | X | | | | |
| 10 | Aware of universal and social effects of software solutions and practices; develops new software tools for solving universal problems and social advance. | | | | X | |
| 11 | Keeps attention in clean and readable code design. | | | X | | |
| 12 | Considers and follows user centered design principles. | X | | | | |
Assessment Methods
| Contribution Level | Absolute Evaluation |
| Rate of Midterm Exam to Success | | 40 |
| Rate of Final Exam to Success | | 60 |
| 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 |
|---|
| DATA SCIENCE | BPR2214995 | Spring Semester | 3+0 | 3 | 5 |
| Prerequisites Courses | |
| Recommended Elective Courses | |
| Language of Course | Turkish |
| Course Level | Short Cycle (Associate's Degree) |
| Course Type | Elective |
| Course Coordinator | Lect. Beyza KOYULMUŞ |
| Name of Lecturer(s) | Lect. Beyza KOYULMUŞ |
| Assistant(s) | |
| Aim | Aims to teach all the methods, processes, algorithms and software applied to extract information from various data. |
| Course Content | This course contains; Introduction to Data Science,Basic concepts of Data Science,Application development stages in data science,Examination of tools used in data science,Creating a data set,Exploratory data analysis operations: review and preparation of the data set,Exploratory data analysis operations: attribute addition and extraction,Exploratory data analysis operations: data filtering, missing data completion,Exploratory data analysis procedures: acquiring basic statistical knowledge,Exploratory data analysis operations: outlier detection,Exploratory data analysis processes: data visualization,Use of machine learning algorithms (classification and clustering),Project Development,Project Development. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Develops a data science application on a data set | 2, 6, 9 | A, E, F |
| Defines the relationship between Data Science and Big Data. | 2, 23, 9 | A, E, F |
| Explains the basic concepts of data science. | 2, 6, 9 | A, E, F |
| Analyzes data sets. | 2, 6, 9 | A, E, F |
| Gains the ability to use data science and modeling tools | 2, 6, 9 | A, E, F |
| Learns how to extract useful information from data | 2, 9 | A, E, F |
| Teaching Methods: | 2: Project Based Learning Model, 23: Concept Map Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|
| 1 | Introduction to Data Science | |
| 2 | Basic concepts of Data Science | |
| 3 | Application development stages in data science | |
| 4 | Examination of tools used in data science | |
| 5 | Creating a data set | |
| 6 | Exploratory data analysis operations: review and preparation of the data set | |
| 7 | Exploratory data analysis operations: attribute addition and extraction | |
| 8 | Exploratory data analysis operations: data filtering, missing data completion | |
| 9 | Exploratory data analysis procedures: acquiring basic statistical knowledge | |
| 10 | Exploratory data analysis operations: outlier detection | |
| 11 | Exploratory data analysis processes: data visualization | |
| 12 | Use of machine learning algorithms (classification and clustering) | |
| 13 | Project Development | |
| 14 | Project Development | |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications |
| No | Program Qualification | Contribution Level |
| 1 | 2 | 3 | 4 | 5 |
| 1 | Has the background in algorithms, programming, and application development in software engineering projects; and has the ability to use them together in business. | | | X | | |
| 2 | Chooses and uses the proper solution methods and special techniques for programming purpose. | | | X | | |
| 3 | Uses modern techniques and tools for programming applications. | | | X | | |
| 4 | Works effectively individually and in teams. | | | X | | |
| 5 | Implements and follows test cases of developed software and applications. | X | | | | |
| 6 | Has the awareness in workplace practices, worker health, environmental and workplace safety, professional and ethical responsibility, and legal issues about programming practices. | X | | | | |
| 7 | Reaches information, and surveys resources for this purpose. | | | | | X |
| 8 | Aware of the necessity of life-long learning; follows technological advances and renews him/herself. | | | X | | |
| 9 | Communicates, oral and written, effectively using modern tools. | X | | | | |
| 10 | Aware of universal and social effects of software solutions and practices; develops new software tools for solving universal problems and social advance. | | | | X | |
| 11 | Keeps attention in clean and readable code design. | | | X | | |
| 12 | Considers and follows user centered design principles. | X | | | | |
Assessment Methods
| Contribution Level | Absolute Evaluation |
| Rate of Midterm Exam to Success | | 40 |
| Rate of Final Exam to Success | | 60 |
| Total | | 100 |
Numerical Data
Ekleme Tarihi: 05/11/2023 - 20:23Son Güncelleme Tarihi: 05/11/2023 - 20:25
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