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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