Istanbul Health and Technology University INFORMATION PACKAGE / COURSE CATALOGUE
| Computer Engineering | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
Course General Introduction Information
| Course Code: | BYM444 | ||||
| Course Name: | Introduction to Data Science | ||||
| Course Semester: |
Spring |
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| Course Credits: |
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| Language of instruction: | |||||
| Course Requirement: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||
| Course Coordinator : | Assoc. Prof. HATİCE ESRA ÖZKAN UÇAR | ||||
| Course Lecturer(s): | - | ||||
| Course Assistants: |
Course Purpose and Content
| Course Objectives: | Teaching of theoretical subjects related to Data Science with application examples in different fields. |
| Course Content: | None |
Learning Outcomes
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The students who have succeeded in this course;
1) Understands data science fundamentals. 2) Learns well-known tutored, uninstructed and semi-supervised learning algorithms. 3) Can apply machine learning techniques to real-world problems. 4) Prepares a project on a subject related to machine learning, writes its report and presents it in class. 5) For a problem with given parameters, the student can reveal the advantages and disadvantages of different machine learning methods. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction to Data Science | None |
| 2) | Decision Trees | None |
| 3) | Example Based Learning | None |
| 4) | Bayesian Learning | None |
| 5) | Logistic Regression | None |
| 6) | Neural Networks | None |
| 7) | Support Vector Machines | None |
| 8) | Clustering, k-means | None |
| 9) | Maximum Expectation, Gaussian Mixture | None |
| 10) | Community Learning | None |
| 11) | Random Forest | None |
| 12) | Adversarial Learning | None |
| 13) | Reinforcement Learning | None |
| 14) | LDA and PCA | None |
Sources
| Course Notes / Textbooks: | Veri Bilimi, John D. Kelleher, Brendan Tierney, The MIT Press,2018 |
| References: | Veri Bilimi, John D. Kelleher, Brendan Tierney, The MIT Press,2018 |
Course - Learning Outcome Relationship
| No Effect | 1 Lowest | 2 Medium | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 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 complex engineering problems. | |
| 2) | Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | |
| 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 | |
| 4) | Ability to devise, select, and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. | |
| 5) | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | |
| 6) | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | |
| 7) | Ability to communicate effectively in Turkish, 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. | |
| 8) | Knowledge of the global and societal impacts of engineering practices on priority issues such as health, environment and safety and contemporary issues; knowledge of the legal aspects of engineering solutions. awareness of the consequences | |
| 9) | Consciousness to behave according to ethical principles and professional and ethical responsibility; knowledge on standards used in engineering practice. | |
| 10) | Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and knowledge about sustainable development. | |
| 11) | Ability to design systems to meet desired needs | |
| 12) | Ability to apply basic sciences in the field of computer engineering | |
| 13) | Ability to implement designs by experiments | |
| 14) | Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. |
Learning Activity and Teaching Methods
| Anlatım | |
| Bireysel çalışma ve ödevi | |
| Course | |
| Grup çalışması ve ödevi |
Measurement and Evaluation Methods and Criteria
| Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama) | |
| Sözlü sınav | |
| Homework | |
| Bireysel Proje |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Homework Assignments | 1 | % 20 |
| Midterms | 1 | % 30 |
| Final | 1 | % 50 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| total | % 100 | |
İş Yükü ve AKTS Kredisi Hesaplaması
| Activities | Number of Activities | Aktiviteye Hazırlık | Aktivitede Harçanan Süre | Aktivite Gereksinimi İçin Süre | Workload | ||
| Course Hours | 14 | 2 | 28 | ||||
| Homework Assignments | 2 | 48 | 96 | ||||
| Midterms | 1 | 24 | 24 | ||||
| Final | 1 | 24 | 24 | ||||
| Total Workload | 172 | ||||||