Software Engineering | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code: | BYM444 | ||||
Course Name: | Introduction to Data Science | ||||
Course Semester: | Fall | ||||
Course Credits: |
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Language of instruction: | |||||
Course Requirement: | |||||
Does the Course Require Work Experience?: | No | ||||
Type of course: | Area Ellective | ||||
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 Objectives: | Teaching of theoretical subjects related to Data Science with application examples in different fields. |
Course Content: | None |
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. |
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 |
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 |
No Effect | 1 Lowest | 2 Medium | 3 Highest |
Program Outcomes | Level of Contribution | |
1) | Sufficient knowledge in mathematics, science and software engineering discipline-specific topics; the theoretical and practical knowledge in these areas, the ability to use in complex engineering problems. | 2 |
2) | The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modelling methods for this purpose. | 2 |
3) | The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. | 3 |
4) | Ability to develop, select and use modern techniques and tools necessary for analysis and solution of complex problems in engineering applications; ability to use information technologies effectively. | 1 |
5) | Ability to design experiments, conduct experiments, collect data, analyse and interpret the results of complex engineering problems or discipline-specific research topics. | 2 |
6) | Disiplin içi ve çok disiplinli takımlarda etkin biçimde çalışabilme becerisi; bireysel çalışma becerisi. | 3 |
7) | Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal. | 1 |
8) | Effective communication skills in Turkish oral and written communication; at least one foreign language knowledge; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give clear and understandable instructions and to receive. | 3 |
9) | Conformity to ethical principles, professional and ethical responsibility; Information on standards used in engineering applications. | 1 |
10) | Information on practices in business, such as project management, risk management and change management; awareness about entrepreneurship, innovation; information on sustainable development. | 2 |
11) | Information on the effects of engineering applications on health, environment, and safety in universal and social dimensions, and on the problems of the modern age in engineering; awareness of the legal consequences of engineering solutions. | 3 |
12) | Adequate skills in the analysis, design, verification, evaluation, implementation, implementation, and maintenance of software systems | 2 |
Anlatım | |
Bireysel çalışma ve ödevi | |
Course | |
Grup çalışması ve ödevi |
Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama) | |
Sözlü sınav | |
Homework | |
Bireysel Proje |
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 |
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 |