DATA SCIENCE PROJECT
- Course
- DASC402 - DATA SCIENCE PROJECT
- Department
- Data Science - English - Undergraduate
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 4
- ECTS
- 8
- T+P+L
- 2 + 0 + 4
- Course Coordinator(s)
- -
- Prerequisite
Course Description
As a continuation of project management course students are expected to complete the structure of the project they started in DASC401, with limited supervision as an independent study. During the project, students should complete the feasibility study by explaining the aims, objectives, tasks, milestones, duration and development of the project. At the end of the project, students will also have information about the subjects of other departments. This way, the student will be able to associate his field of specialization with the subjects of other departments. Upon completion of the project, the student is required to submit a project report and conduct an oral presentation.
DATA SCIENCE PROJECT
Evaluation Tools (Active Term)
No evaluation items have been defined.
Course outcomes
No course outcomes have been defined yet.
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Dersin Tanıtımı ve MIS Projelerine Genel Bakış |
| Week 2 | Kurumsal Çerçeveler ve Teorik Temeller |
| Week 3 | Proje Yönetimi Teknikleri ve Araçları |
| Week 4 | Bilişim Sistemi Tasarımı ve Geliştirme Stratejileri |
| Week 5 | Teknoloji Yeniliği ve MIS Stratejisi Hizalaması |
| Week 6 | MIS'de Etik Değerlendirmeler |
| Week 7 | Veri Toplama ve Analiz Teknikleri |
| Week 8 | Vize Sınav Dönemi |
| Week 9 | Vize Sınav Dönemi |
| Week 10 | Sorun Çözme için İleri Analitik Teknikler |
| Week 11 | Profesyonel Ortamlarda Etkili İletişim |
| Week 12 | Proje Yönetiminde İşbirliği ve Liderlik |
| Week 13 | Proje Uygulaması ve Yönetimi |
| Week 14 | Final Sunumları ve Raporları için Hazırlık |
| Week 15 | Final Sunumları ve Dersin Kapanışı |
Reference Books & Course Materials
No reference books have been listed.
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- P01 Apply data science principles and techniques to challenges in real life situations, and effectively communicate their solutions.
- P02 Identify and implement data analysis methodologies based on theoretical ideas, ethical code, and in-depth knowledge of the underlying data.
- P03 Analyze the guiding concepts and assessment procedures for information analysis in real-life applications.
- P04 Design and apply relevant data analysis models to find obscure solutions to business-related problems.
- P05 Utilize modern computing techniques to handle real-world problems characterized by massive amounts of data, such as parallel and distributed computing and machine learning.
- P06 Configure and administer the software tools required to efficiently produce usable information from any size of structured and unstructured datasets.
- P07 Administer or manage data science tools and techniques to organize and complete projects aimed at gaining useful insight from complex data.
- P08 Think critically and imaginatively, conceiving real-world issues from several angles, and work well in a variety of teams to solve issues cooperatively.
- P09 Be able to effectively integrate data‐based solutions into the user environment and help non-technical professionals in exploring, visualizing, and using these solutions
- P10 Understand their obligations under professional and ethical standards in relation to matters like data ownership and citation, data security and sensitivity and the privacy implications of data analysis.
Po-Lo Matrix
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