Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| DATA SCIENCE | - | Fall Semester | 3+0 | 3 | 6 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Reda ALHAJJ |
| Name of Lecturer(s) | Prof.Dr. Reda ALHAJJ |
| Assistant(s) | |
| Aim | This course introduces the basics of data science as the rapidly emerging most popular domain for researchers and practitioners in the 21st century. It highlights the basic skills to be acquired by a data scientist with various applications from medicine, homeland security, engineering, finance, etc. The objectives of the course are (1) introducing the concept of knowledge discovery in data and discuss the steps to be followed including the problem definition, data collection, integration and management, data analysis, and visualization. (2) highlighting the importance of dealing with various aspects of data, including volume, variety, velocity, veracity, value, etc., (3) introducing the basic statistical and machine learning techniques which could be effectively used for knowledge discovery, (4) covering network modeling and graph analysis as powerful alternative mechanisms for making sense from data, (5) illustrating how data visualize is effective for communication, and (6) covering basics of recommendation systems. |
| Course Content | This course contains; Introduction to Data Science, probability, statistics, linear algebra,Basic data models, Entity-Relationship model, Relational Model and SQL.,From SQL to NoSQL, non-relational databases and related data models, XML Model and Xquery. ,NoSQL Databases, the case of Mongo DB. ,Sources and types of big data, frequent pattern analysis. ,Presentations by students research articles / tools. ,Presentations by students research articles / tools. ,Midterm overview,Clustering,Classification, Incremental data analysis and Scalable methods for Data management and analysis. ,Network model and graph analysis. ,Data visualization,Recommendation systems. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Understanding of basic network modeling and graph analysis techniques to handle data science tasks. | 2, 9 | A, E, F, G |
| 1. Understanding the basics of data science and the skill sets distinguishing a data scientist. | 2, 9 | A, E, F, G |
| 2. Understanding the basics of data collection, modeling and management for data science tasks. | 2, 9 | A, E, F, G |
| 3. Understanding of basic statistical modeling and analysis for data science tasks. | 2, 9 | A, E, F, G |
| 4. Understanding basic machine learning algorithms and techniques needed to cope with data science tasks. | 2, 9 | A, E, F, G |
| 4. Understanding basic machine learning algorithms and techniques needed to cope with data science tasks. | 2, 9 | A, E, F, G |
| 6. Understanding of basic approaches to visualize data for effective communication and understanding. | 2, 9 | A, E, F, G |
| Teaching Methods: | 2: Project Based Learning Model, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task, G: Quiz |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction to Data Science, probability, statistics, linear algebra | Lecture Notes, Week 1. |
| 2 | Basic data models, Entity-Relationship model, Relational Model and SQL. | Lecture Notes, Week 2. |
| 3 | From SQL to NoSQL, non-relational databases and related data models, XML Model and Xquery. | Lecture Notes, Week 3. |
| 4 | NoSQL Databases, the case of Mongo DB. | Lecture Notes, Week 4. |
| 5 | Sources and types of big data, frequent pattern analysis. | Lecture Notes, Week 5. |
| 6 | Presentations by students research articles / tools. | Literature survey. |
| 7 | Presentations by students research articles / tools. | Literature survey. |
| 8 | Midterm overview | All the topics till Week 8. |
| 9 | Clustering | Lecture Notes, Week 9. |
| 10 | Classification | Lecture Notes, Week 10. |
| 11 | Incremental data analysis and Scalable methods for Data management and analysis. | Lecture Notes, Week 11. |
| 12 | Network model and graph analysis. | Lecture Notes, Week 12. |
| 13 | Data visualization | Lecture Notes, Week 13. |
| 14 | Recommendation systems | Lecture Notes, Week 14 |
| Resources |
| No specific text book, notes will be made available, including in class notes, (sometimes) slides, research papers, book chapters, etc. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. | X | |||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | X | |||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | X | |||||
| 4 | Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. | X | |||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | X | |||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | X | |||||
| 7 | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | X | |||||
| 8 | Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. | X | |||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | X | |||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | ||||||
| 11 | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 14 | 3 | 42 | |||
| Guided Problem Solving | 0 | 0 | 0 | |||
| Resolution of Homework Problems and Submission as a Report | 5 | 8 | 40 | |||
| Term Project | 0 | 0 | 0 | |||
| Presentation of Project / Seminar | 2 | 24 | 48 | |||
| Quiz | 5 | 1 | 5 | |||
| Midterm Exam | 1 | 24 | 24 | |||
| General Exam | 1 | 24 | 24 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 183 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(183/30) | 6 | |||||
| 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 | - | Fall Semester | 3+0 | 3 | 6 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Reda ALHAJJ |
| Name of Lecturer(s) | Prof.Dr. Reda ALHAJJ |
| Assistant(s) | |
| Aim | This course introduces the basics of data science as the rapidly emerging most popular domain for researchers and practitioners in the 21st century. It highlights the basic skills to be acquired by a data scientist with various applications from medicine, homeland security, engineering, finance, etc. The objectives of the course are (1) introducing the concept of knowledge discovery in data and discuss the steps to be followed including the problem definition, data collection, integration and management, data analysis, and visualization. (2) highlighting the importance of dealing with various aspects of data, including volume, variety, velocity, veracity, value, etc., (3) introducing the basic statistical and machine learning techniques which could be effectively used for knowledge discovery, (4) covering network modeling and graph analysis as powerful alternative mechanisms for making sense from data, (5) illustrating how data visualize is effective for communication, and (6) covering basics of recommendation systems. |
| Course Content | This course contains; Introduction to Data Science, probability, statistics, linear algebra,Basic data models, Entity-Relationship model, Relational Model and SQL.,From SQL to NoSQL, non-relational databases and related data models, XML Model and Xquery. ,NoSQL Databases, the case of Mongo DB. ,Sources and types of big data, frequent pattern analysis. ,Presentations by students research articles / tools. ,Presentations by students research articles / tools. ,Midterm overview,Clustering,Classification, Incremental data analysis and Scalable methods for Data management and analysis. ,Network model and graph analysis. ,Data visualization,Recommendation systems. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Understanding of basic network modeling and graph analysis techniques to handle data science tasks. | 2, 9 | A, E, F, G |
| 1. Understanding the basics of data science and the skill sets distinguishing a data scientist. | 2, 9 | A, E, F, G |
| 2. Understanding the basics of data collection, modeling and management for data science tasks. | 2, 9 | A, E, F, G |
| 3. Understanding of basic statistical modeling and analysis for data science tasks. | 2, 9 | A, E, F, G |
| 4. Understanding basic machine learning algorithms and techniques needed to cope with data science tasks. | 2, 9 | A, E, F, G |
| 4. Understanding basic machine learning algorithms and techniques needed to cope with data science tasks. | 2, 9 | A, E, F, G |
| 6. Understanding of basic approaches to visualize data for effective communication and understanding. | 2, 9 | A, E, F, G |
| Teaching Methods: | 2: Project Based Learning Model, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task, G: Quiz |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction to Data Science, probability, statistics, linear algebra | Lecture Notes, Week 1. |
| 2 | Basic data models, Entity-Relationship model, Relational Model and SQL. | Lecture Notes, Week 2. |
| 3 | From SQL to NoSQL, non-relational databases and related data models, XML Model and Xquery. | Lecture Notes, Week 3. |
| 4 | NoSQL Databases, the case of Mongo DB. | Lecture Notes, Week 4. |
| 5 | Sources and types of big data, frequent pattern analysis. | Lecture Notes, Week 5. |
| 6 | Presentations by students research articles / tools. | Literature survey. |
| 7 | Presentations by students research articles / tools. | Literature survey. |
| 8 | Midterm overview | All the topics till Week 8. |
| 9 | Clustering | Lecture Notes, Week 9. |
| 10 | Classification | Lecture Notes, Week 10. |
| 11 | Incremental data analysis and Scalable methods for Data management and analysis. | Lecture Notes, Week 11. |
| 12 | Network model and graph analysis. | Lecture Notes, Week 12. |
| 13 | Data visualization | Lecture Notes, Week 13. |
| 14 | Recommendation systems | Lecture Notes, Week 14 |
| Resources |
| No specific text book, notes will be made available, including in class notes, (sometimes) slides, research papers, book chapters, etc. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. | X | |||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | X | |||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | X | |||||
| 4 | Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. | X | |||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | X | |||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | X | |||||
| 7 | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | X | |||||
| 8 | Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. | X | |||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | X | |||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | ||||||
| 11 | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| Total | 100 | |