BIG DATA ANALYTICS AND MANAGEMENT (ENGLISH, NONTHESIS)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

Course Introduction and Application Information

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.

Basic information

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.

Learning Outcomes

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.

Course Content

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.

Weekly Detailed Course Contents

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

Sources

Course Notes / Textbooks:	None
References:	-

Evaluation System

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

ECTS / Workload Table

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

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	To be able to follow and critically analyze scientific literature and use it effectively in solving engineering problems.	5
2)	To be able to design, plan, implement and manage original projects related to Big Data Analytics and Management.	3
3)	To be able to carry out studies on Big Data Analytics and Management independently, take scientific responsibility and critically evaluate the results obtained.	1
4)	Effectively present the results of his/her research and projects in written, oral and visual form in accordance with academic standards.	2
5)	To be able to conduct independent research in the field of Big Data Analytics and Management, develop original ideas and transfer this knowledge to practice.	2
6)	Uses advanced theoretical and practical knowledge specific to the field of Big Data Analytics and Management effectively.	2
7)	Acts in accordance with professional, scientific and ethical values; takes responsibility by considering the social, environmental and ethical impacts of engineering applications.	5