| INDUSTRIAL ENGINEERING (ENGLISH, THESIS) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| BDA5121 | Enterpreneurship and Managing Big Data | Fall | 3 | 0 | 3 | 8 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
| Language of instruction: | English |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Assist. Prof. ECE GELAL SOYAK |
| Course Objectives: | The primary objective of this course is to equip students with the knowledge and skills to identify, evaluate, and capitalize on entrepreneurial opportunities driven by big data. Students will explore how data can serve as both a strategic asset and a catalyst for innovation in startups and data-centric ventures. The course aims to bridge the gap between technical big data capabilities and business acumen, encouraging students to think critically about data monetization, digital business models, and the role of data in creating scalable, sustainable enterprises. Through case studies, real-world examples, and hands-on project work, students will learn to formulate data-driven business ideas and understand the managerial challenges of implementing data strategies in dynamic, high-growth environments. |
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The students who have succeeded in this course; Students who complete this course can; - Understand how big data can be leveraged to identify and validate entrepreneurial opportunities. - Apply lean startup principles in the context of data-driven business models. - Design and evaluate business models that incorporate big data as a core value driver. - Develop strategies for managing, governing, and monetizing data assets in startup environments. - Analyze case studies of successful data-driven ventures and extract key success factors. - Address ethical, legal, and regulatory challenges related to data use in entrepreneurial contexts. - Communicate business ideas effectively to stakeholders, including investors, partners, and technical teams. |
| The course covers a wide range of interdisciplinary topics at the intersection of entrepreneurship and big data. Early modules introduce the foundations of entrepreneurial thinking, opportunity recognition, and lean startup methodologies, followed by in-depth discussions on the role of big data in value creation and business model innovation. Students will study data lifecycle management, data governance, and the ethical use of data in entrepreneurial ventures. Emphasis is placed on practical applications, including identifying data-driven opportunities, prototyping data products, and designing go-to-market strategies. Additional topics include investor perspectives on data-driven startups, scaling data infrastructure, and navigating regulatory and privacy challenges in digital businesses. |
| Week | Subject | Related Preparation |
| 1) | Introduction to Entrepreneurship and Big Data | |
| 2) | Identifying market gaps using data analytics; validating startup ideas with customer and industry data. | |
| 3) | Lean Startup and Data-Driven Validation; Minimum viable product (MVP) and A/B testing with data | |
| 4) | Overview of big data ecosystems (Hadoop, Spark, cloud platforms) | |
| 5) | Business Models and Data Monetization | |
| 6) | Data Governance and Ethics in Startups | |
| 7) | Prototyping and Product Development with Data | |
| 8) | Transitioning from MVP to scale: challenges and solutions | |
| 9) | Funding Data-Driven Startups | |
| 10) | Competitive Advantage and Data Network Effects | |
| 11) | Final project development | |
| 12) | Final Project Presentations |
| Course Notes / Textbooks: | None |
| References: | - |
| Semester Requirements | Number of Activities | Level of Contribution |
| Quizzes | 4 | % 40 |
| Presentation | 1 | % 40 |
| Paper Submission | 1 | % 20 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 100 | |
| PERCENTAGE OF FINAL WORK | % | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Presentations / Seminar | 1 | 30 | 30 |
| Project | 1 | 50 | 50 |
| Quizzes | 3 | 15 | 45 |
| Paper Submission | 1 | 30 | 30 |
| Total Workload | 197 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Follows the scientific literature, analyzes it critically, and uses it effectively in solving engineering problems. | |
| 2) | Designs, plans, implements, and manages original projects related to the program field. | |
| 3) | Independently conducts studies related to the program field, assumes scientific responsibility, and evaluates the results with a critical perspective. | |
| 4) | Presents the results of their research and projects effectively in written, oral, and visual formats in accordance with academic standards. | |
| 5) | Conducts independent research on subjects requiring expertise in their field, develops original ideas, and transfers this knowledge into practice. | |
| 6) | Effectively uses advanced theoretical and practical knowledge specific to the program field. | |
| 7) | Acts in accordance with professional, scientific, and ethical values; takes responsibility by considering the social, environmental, and ethical impacts of engineering practices. |