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 of Media and Visual Arts disciplines. | X | |||||
| 2 | Knows the basic theories of media and visual arts disciplines. | X | |||||
| 3 | Knows the necessary computer programs and multi-media techniques in Media and Visual Arts. | X | |||||
| 4 | Knows the aesthetic rules necessary in Media and Visual Arts. | X | |||||
| 5 | Acts in a way that adheres to national and international ethical codes in professional and scientific studies. | X | |||||
| 6 | Understands the symbol systems of cultures. | X | |||||
| 7 | Analyzes the facts related to Media and Visual Arts in their dimensions. | X | |||||
| 8 | Plans the visual design process in line with the needs of institutions/individuals. | X | |||||
| 9 | Carries out the visual communication process in line with the needs of institutions/individuals. | X | |||||
| 10 | Uses the tools, methods and techniques required for Media and Visual Arts practices. | X | |||||
| 11 | Has the ability to produce, process and evaluate real, 2D and 3D images. | ||||||
| 12 | Applies visual design techniques in new media environments. | ||||||
| 13 | Has aesthetic awareness and understanding of design. | ||||||
| 14 | Performs their profession by taking into consideration the "Occupational Health and Safety" rules. | ||||||
| 15 | Solve problems that arise by taking responsibility in projects related to Media and Visual Arts. | X | |||||
| 16 | Can transform theoretical and factual problems of Media and Visual Arts disciplines and sub-disciplines into publications using scientific methods. | X | |||||
| 17 | Regularly follows the developments in the field of Media and Visual Arts and uses them effectively in her work. | ||||||
| 18 | Uses Turkish fluently and accurately in scientific and professional studies. | ||||||
| 19 | Can read and understand at least one foreign language at B1 level. | ||||||
| 20 | Plans and takes part in social responsibility projects. | X | |||||
| 21 | Incorporates artificial intelligence (artificial learning/machine learning) into the creation process. | 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 | 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 of Media and Visual Arts disciplines. | X | |||||
| 2 | Knows the basic theories of media and visual arts disciplines. | X | |||||
| 3 | Knows the necessary computer programs and multi-media techniques in Media and Visual Arts. | X | |||||
| 4 | Knows the aesthetic rules necessary in Media and Visual Arts. | X | |||||
| 5 | Acts in a way that adheres to national and international ethical codes in professional and scientific studies. | X | |||||
| 6 | Understands the symbol systems of cultures. | X | |||||
| 7 | Analyzes the facts related to Media and Visual Arts in their dimensions. | X | |||||
| 8 | Plans the visual design process in line with the needs of institutions/individuals. | X | |||||
| 9 | Carries out the visual communication process in line with the needs of institutions/individuals. | X | |||||
| 10 | Uses the tools, methods and techniques required for Media and Visual Arts practices. | X | |||||
| 11 | Has the ability to produce, process and evaluate real, 2D and 3D images. | ||||||
| 12 | Applies visual design techniques in new media environments. | ||||||
| 13 | Has aesthetic awareness and understanding of design. | ||||||
| 14 | Performs their profession by taking into consideration the "Occupational Health and Safety" rules. | ||||||
| 15 | Solve problems that arise by taking responsibility in projects related to Media and Visual Arts. | X | |||||
| 16 | Can transform theoretical and factual problems of Media and Visual Arts disciplines and sub-disciplines into publications using scientific methods. | X | |||||
| 17 | Regularly follows the developments in the field of Media and Visual Arts and uses them effectively in her work. | ||||||
| 18 | Uses Turkish fluently and accurately in scientific and professional studies. | ||||||
| 19 | Can read and understand at least one foreign language at B1 level. | ||||||
| 20 | Plans and takes part in social responsibility projects. | X | |||||
| 21 | Incorporates artificial intelligence (artificial learning/machine learning) into the creation process. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 40 | |
| Rate of Final Exam to Success | 60 | |
| Total | 100 | |