| ARTIFICIAL INTELLIGENCE ENGINEERING | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| AIN2002 | Introduction to Data Science | Spring | 2 | 2 | 3 | 6 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Must Course |
| Course Level: | Bachelor |
| Mode of Delivery: | Hybrid |
| Course Coordinator : | Instructor MUSTAFA ÜMİT ÖNER |
| Course Objectives: | The aim of the course is to give students theoretical knowledge and application skills in the field of data science. |
|
The students who have succeeded in this course; Students will be able to develop solutions to data science problems and evaluate the success of the solutions they develop by applying these techniques. At the end of the course, you will: 1) To be able to define data science problems 2) To be able to apply data collection, cleaning and preparation techniques used in data science 3) To be able to perform explanatory data analysis and visualization on datasets 4) To be able to apply the necessary methods for extracting and selecting features on datasets 5) To recognize the basic problem types in data science and to be able to choose the methods used for their solutions 6) To evaluate the success and performance of data science solutions |
| For this purpose, basic techniques used in data science and basic data science problems will be introduced. |
| Week | Subject | Related Preparation | |
| 1) | Basic Concepts | ||
| 2) | Data Exploration | ||
| 3) | Data Pre-Processing, Cleaning, Preparation | ||
| 4) | Dimensionality Reduction: Feature Extraction | ||
| 5) | Dimensionality Reduction: Feature Selection | ||
| 6) | Supervised Learning: Model Selection and Generalization | ||
| 7) | Supervised Learning Algorithms | ||
| 8) | Midterm Exam | ||
| 9) | Unsupervised Learning (Clustering) | ||
| 10) | Unsupervised Learning (Clustering) | ||
| 11) | Anomaly Detection | ||
| 12) | Text Mining | ||
| 13) | Project Presentation | ||
| 14) | Project Presentation | ||
| Course Notes: | Textbook: Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar Reference Book: Introduction to Machine Learning by Ethem Alpaydın, The MIT Press |
| References: | Textbook: Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar Reference Book: Introduction to Machine Learning by Ethem Alpaydın, The MIT Press |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | 8 | % 16 |
| Homework Assignments | % 0 | |
| Presentation | % 0 | |
| Project | 1 | % 20 |
| Seminar | % 0 | |
| Midterms | 1 | % 24 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 2 | 28 |
| Laboratory | 14 | 2 | 28 |
| Application | 0 | 0 | 0 |
| Special Course Internship (Work Placement) | 0 | 0 | 0 |
| Field Work | 0 | 0 | 0 |
| Study Hours Out of Class | 0 | 0 | 0 |
| Presentations / Seminar | 0 | 0 | 0 |
| Project | 1 | 30 | 30 |
| Homework Assignments | 0 | 0 | 0 |
| Quizzes | 8 | 2 | 16 |
| Preliminary Jury | 0 | 0 | 0 |
| Midterms | 1 | 22 | 22 |
| Paper Submission | 0 | 0 | 0 |
| Jury | 0 | 0 | 0 |
| Final | 1 | 28 | 28 |
| Total Workload | 152 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Have sufficient background in mathematics, science and artificial intelligence engineering. | 5 |
| 2) | Use theoretical and applied knowledge in the fields of mathematics, science and artificial intelligence engineering together for engineering solutions. | 5 |
| 3) | Identify, define, formulate and solve engineering problems, select and apply appropriate analytical methods and modeling techniques for this purpose. | 5 |
| 4) | Analyse a system, system component or process and design it under realistic constraints to meet desired requirements; apply modern design methods in this direction. | 5 |
| 5) | Select and use modern techniques and tools necessary for engineering applications. | 5 |
| 6) | Design and conduct experiments, collect data, and analyse and interpret results. | 5 |
| 7) | Work effectively both as an individual and as a multi-disciplinary team member. | |
| 8) | Access information via conducting literature research, using databases and other resources | |
| 9) | Follow the developments in science and technology and constantly update themself with an awareness of the necessity of lifelong learning. | |
| 10) | Use information and communication technologies together with computer software with at least the European Computer License Advanced Level required by their field. | |
| 11) | Communicate effectively, both verbal and written; know a foreign language at least at the European Language Portfolio B1 General Level. | |
| 12) | Have an awareness of the universal and social impacts of engineering solutions and applications; know about entrepreneurship and innovation; and have an awareness of the problems of the age. | |
| 13) | Have a sense of professional and ethical responsibility. | |
| 14) | Have an awareness of project management, workplace practices, employee health, environment and work safety; know the legal consequences of engineering practices. |