Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| DATA MINING | - | Fall Semester | 1+2 | 2 | 5 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Assoc.Prof. Başak GEZMEN |
| Name of Lecturer(s) | Lect. Zafer DEMİRKOL |
| Assistant(s) | |
| Aim | To learn the basic concepts of data mining and to obtain useful information by drawing meaningful conclusions from the data. |
| Course Content | This course contains; Data Science, Getting to know the course and explaining the content,Definition of data, working with datasets,Manipulating Datasets with the Pandas Library,Analysis and Summarization of Data,Data Visualization definition and key components,Exploratory Data Analysis techniques,Application: Data Collection, Exploratory Data Analysis,Application: Data Visualization applications,Machine learning fundamentals,machine learning algorithms,Application: Linear Regression,Classification Problems,Application: Classification Application,Deep learning basic concepts. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Understands Data Collection methods. | 16, 6, 9 | A, E |
| Performs Data Sets Management analysis. | 16, 6, 9 | A, E |
| Analyzes Data Visualization categories | 16, 6, 9 | E |
| Teaching Methods: | 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Data Science, Getting to know the course and explaining the content | Reading the topics mentioned from relevant sources |
| 2 | Definition of data, working with datasets | Reading the topics mentioned from relevant sources |
| 3 | Manipulating Datasets with the Pandas Library | Reading the topics mentioned from relevant sources |
| 4 | Analysis and Summarization of Data | Reading the topics mentioned from relevant sources |
| 5 | Data Visualization definition and key components | Reading the topics mentioned from relevant sources |
| 6 | Exploratory Data Analysis techniques | Reading the topics mentioned from relevant sources |
| 7 | Application: Data Collection, Exploratory Data Analysis | Reading the topics mentioned from relevant sources |
| 8 | Application: Data Visualization applications | Reading the topics mentioned from relevant sources |
| 9 | Machine learning fundamentals | Reading the topics mentioned from relevant sources |
| 10 | machine learning algorithms | Reading the topics mentioned from relevant sources |
| 11 | Application: Linear Regression | Reading the topics mentioned from relevant sources |
| 12 | Classification Problems | Reading the topics mentioned from relevant sources |
| 13 | Application: Classification Application | Reading the topics mentioned from relevant sources |
| 14 | Deep learning basic concepts | Reading the topics mentioned from relevant sources |
| Resources |
| Florin Gorunescu. Data Mining- Concepts, Models and Techniques. Springer Publishing, 2011 Graham J. WilliamsSimeon J. Simoff, Data Mining Theory, Methodology, Techniques, and Applications, Springer, 2006 Joel Grus , Data Science from Scratch: First Principles with Python, O'Reilly Media, 2019 |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Knows the basic concepts and theoretical grounds related to the field. | X | |||||
| 2 | Determines the facts related to New Media and Communication Systems and analyzes these facts with various dimensions. | X | |||||
| 3 | Analyzes the needs of the media organizations and plans and applies strategies accordingly. | X | |||||
| 4 | Plans new media projects and implements them. | X | |||||
| 5 | Takes responsibility when necessary in the field related projects and proposes solutions to emerging problems. | X | |||||
| 6 | Takes place as a member in a project-based teamwork; leads projects and plans events. | X | |||||
| 7 | Observes the theoretical and factual problems with scientific methods related to new media and communication systems disciplines and sub-disciplines; analyzes the findings and presents them in scientific publications. | X | |||||
| 8 | Has a high awareness towards lifelong learning. Follows the developments, innovations, opinions, methods and techniques regularly and uses them efficiently. | X | |||||
| 9 | To be able to communicate orally and in writing in a foreign language at least at the B1 level of the European Language Portfolio. | X | |||||
| 10 | Utilizes new communication technologies efficiently in professional and scientific works and follows the developments in new communication technologies regularly. | X | |||||
| 11 | Plans social responsibility events and takes a role in implementation process. | X | |||||
| 12 | Acts in accordance with ethical codes in professional and scientific works. | X | |||||
| 13 | Uses tools related to new media and communications, efficiently. | X | |||||
| 14 | Develops and implement new media projects specifically developed for media organizations. | X | |||||
| 15 | Sensitive to the environment, the universality of social rights and the protection of cultural values. | ||||||
| 16 | Knowledgeable about occupational health and safety and can use this information when necessary. | ||||||
| 17 | Uses Turkish language fluently and accurately in scientific and professional works. | ||||||
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 | 14 | 3 | 42 | |||
| Guided Problem Solving | 20 | 1 | 20 | |||
| Resolution of Homework Problems and Submission as a Report | 20 | 1 | 20 | |||
| Term Project | 14 | 2 | 28 | |||
| Presentation of Project / Seminar | 0 | 0 | 0 | |||
| Quiz | 0 | 0 | 0 | |||
| Midterm Exam | 1 | 20 | 20 | |||
| General Exam | 1 | 20 | 20 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 150 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(150/30) | 5 | |||||
| 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 MINING | - | Fall Semester | 1+2 | 2 | 5 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Assoc.Prof. Başak GEZMEN |
| Name of Lecturer(s) | Lect. Zafer DEMİRKOL |
| Assistant(s) | |
| Aim | To learn the basic concepts of data mining and to obtain useful information by drawing meaningful conclusions from the data. |
| Course Content | This course contains; Data Science, Getting to know the course and explaining the content,Definition of data, working with datasets,Manipulating Datasets with the Pandas Library,Analysis and Summarization of Data,Data Visualization definition and key components,Exploratory Data Analysis techniques,Application: Data Collection, Exploratory Data Analysis,Application: Data Visualization applications,Machine learning fundamentals,machine learning algorithms,Application: Linear Regression,Classification Problems,Application: Classification Application,Deep learning basic concepts. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Understands Data Collection methods. | 16, 6, 9 | A, E |
| Performs Data Sets Management analysis. | 16, 6, 9 | A, E |
| Analyzes Data Visualization categories | 16, 6, 9 | E |
| Teaching Methods: | 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Data Science, Getting to know the course and explaining the content | Reading the topics mentioned from relevant sources |
| 2 | Definition of data, working with datasets | Reading the topics mentioned from relevant sources |
| 3 | Manipulating Datasets with the Pandas Library | Reading the topics mentioned from relevant sources |
| 4 | Analysis and Summarization of Data | Reading the topics mentioned from relevant sources |
| 5 | Data Visualization definition and key components | Reading the topics mentioned from relevant sources |
| 6 | Exploratory Data Analysis techniques | Reading the topics mentioned from relevant sources |
| 7 | Application: Data Collection, Exploratory Data Analysis | Reading the topics mentioned from relevant sources |
| 8 | Application: Data Visualization applications | Reading the topics mentioned from relevant sources |
| 9 | Machine learning fundamentals | Reading the topics mentioned from relevant sources |
| 10 | machine learning algorithms | Reading the topics mentioned from relevant sources |
| 11 | Application: Linear Regression | Reading the topics mentioned from relevant sources |
| 12 | Classification Problems | Reading the topics mentioned from relevant sources |
| 13 | Application: Classification Application | Reading the topics mentioned from relevant sources |
| 14 | Deep learning basic concepts | Reading the topics mentioned from relevant sources |
| Resources |
| Florin Gorunescu. Data Mining- Concepts, Models and Techniques. Springer Publishing, 2011 Graham J. WilliamsSimeon J. Simoff, Data Mining Theory, Methodology, Techniques, and Applications, Springer, 2006 Joel Grus , Data Science from Scratch: First Principles with Python, O'Reilly Media, 2019 |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Knows the basic concepts and theoretical grounds related to the field. | X | |||||
| 2 | Determines the facts related to New Media and Communication Systems and analyzes these facts with various dimensions. | X | |||||
| 3 | Analyzes the needs of the media organizations and plans and applies strategies accordingly. | X | |||||
| 4 | Plans new media projects and implements them. | X | |||||
| 5 | Takes responsibility when necessary in the field related projects and proposes solutions to emerging problems. | X | |||||
| 6 | Takes place as a member in a project-based teamwork; leads projects and plans events. | X | |||||
| 7 | Observes the theoretical and factual problems with scientific methods related to new media and communication systems disciplines and sub-disciplines; analyzes the findings and presents them in scientific publications. | X | |||||
| 8 | Has a high awareness towards lifelong learning. Follows the developments, innovations, opinions, methods and techniques regularly and uses them efficiently. | X | |||||
| 9 | To be able to communicate orally and in writing in a foreign language at least at the B1 level of the European Language Portfolio. | X | |||||
| 10 | Utilizes new communication technologies efficiently in professional and scientific works and follows the developments in new communication technologies regularly. | X | |||||
| 11 | Plans social responsibility events and takes a role in implementation process. | X | |||||
| 12 | Acts in accordance with ethical codes in professional and scientific works. | X | |||||
| 13 | Uses tools related to new media and communications, efficiently. | X | |||||
| 14 | Develops and implement new media projects specifically developed for media organizations. | X | |||||
| 15 | Sensitive to the environment, the universality of social rights and the protection of cultural values. | ||||||
| 16 | Knowledgeable about occupational health and safety and can use this information when necessary. | ||||||
| 17 | Uses Turkish language fluently and accurately in scientific and professional works. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 40 | |
| Rate of Final Exam to Success | 60 | |
| Total | 100 | |