DATA SCIENCE CONCEPTS AND PRACTICES
- Course
- DASC501 - DATA SCIENCE CONCEPTS AND PRACTICES
- Department
- Data Science - English - Master
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 8
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- Asst. Prof. Dr. Yasemin BAY
- Prerequisite
- -
Course Description
The concepts of data science will be covered throughout the course from a variety of angles, including conceptual formulation and properties, solution algorithms and their applications, data visualization for exploratory data analysis, and the appropriate presentation of modeling outcomes. With the use of real-world examples, students will understand the purpose, effectiveness, and constraints of models. Upon completion of the course, students will be able to comprehend the contemporary data science landscape and technical terminology, identify key concepts and tools in the field of data science and determine when they can be applied effectively. Students will also be able to recognize the significance of curating, organizing, and wrangling data, explain uncertainty, causality, and data quality and anticipate the effects of data use and misconduct.
DATA SCIENCE CONCEPTS AND PRACTICES
Evaluation Tools (Active Term)
| Item | Type | Weight (%) |
|---|---|---|
| Midterm | Midterm | 30 |
| Final | Final | 40 |
| Project | Project | 20 |
| Assignment | Assignment | 10 |
| Total | 100 | |
Course outcomes
No course outcomes have been defined yet.
Course Syllabus
No weekly content has been defined yet.
Reference Books & Course Materials
No reference books have been listed.
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- Demonstrate advanced knowledge of core concepts and theoretical frameworks in Management Information Systems, and apply them to develop innovative models that reflect current advancements in the field.
- Analyze complex organizational problems and design integrated information systems solutions by applying advanced MIS knowledge and synthesizing multiple theoretical perspectives.
- Develop and manage database systems and data-driven applications that ensure effective storage, retrieval, and use of organizational data in real-world contexts.
- Utilize data analytics, business intelligence tools, and data visualization techniques to extract actionable insights and support strategic, data-driven decision-making.
- Evaluate and apply emerging technologies such as AI, IoT, and Blockchain to drive digital transformation, enhance organizational performance, and formulate original research inquiries.
- Conduct independent and collaborative research using appropriate methodologies, and communicate findings effectively through scholarly writing and presentations suited to diverse audiences.
- dentify and address ethical, legal, and societal implications in the research, development, and use of information technologies, adhering to scientific and professional standards.
- Collaborate effectively in multidisciplinary teams and demonstrate leadership in addressing complex technology-driven challenges.
- Apply project management methodologies and tools to plan, execute, and monitor IT projects within organizational settings.
- Develop and integrate information systems that align with organizational goals and strategies, supporting innovation and sustainable growth.
- Perform comprehensive systems analysis and design, including requirements gathering, modeling, prototyping, and evaluating enterprise system architectures such as ERP, CRM, and SCM.
- Assess and mitigate organizational information security risks by applying appropriate principles, technologies, and best practices in cybersecurity.
Po-Lo Matrix
The PO-LO matrix has not been populated yet.