EXECUTIVE MBA (TURKISH, 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
ISL5130 Big Data Science and Management Fall
Spring
3 0 3 8
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: Tr
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Assist. Prof. SERKAN YEŞİLYURT
Course Objectives: The aim of this course is to provide students with a comprehensive understanding of the concept of big data, including how large-scale data is collected, processed, analyzed, and transformed into meaningful insights. Through theoretical and hands-on learning, students will explore various data sources, work with structured and unstructured data, and gain skills in data cleaning, data mining, visualization, and basic machine learning techniques. The course also introduces major big data infrastructures such as Hadoop and Spark, focusing on their architecture and practical applications. An important objective is to enable students to critically evaluate big data practices through the lens of ethics, privacy, and data security. By engaging with real-world datasets, students will develop practical analytical thinking and problem-solving skills aligned with current industry and research trends.

Learning Outputs

The students who have succeeded in this course;
Students who successfully complete this course will:

1. Explain the concept of big data, its characteristics, and its relationship to data science.

2. Recognize and analyze structured and unstructured data types.

3. Use big data processing tools and platforms (e.g., Hadoop, Spark) at a basic level.

4. Perform data cleaning, visualization, and analysis using programming languages such as Python.

5. Apply basic machine learning techniques to big data analytics.

6. Develop data-driven solutions for real-world problems.

7. Develop awareness of data security, privacy, and ethical issues.

Course Content

This course provides a comprehensive introduction to the concept, significance, and applications of big data in relation to data science. It covers working with structured and unstructured data, data collection and preprocessing, big data infrastructures such as Hadoop and Spark, data mining, data visualization, and basic machine learning implementations. The course also addresses key issues related to ethics, privacy, and data security. Practical sessions will include tools such as Python and Jupyter Notebook, allowing students to engage in hands-on analysis. Real-world datasets will be used to support project-based learning and practical exploration.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) What is Big Data? Definition and History None
2) The 5Vs of Big Data (Volume, Velocity, etc.) None
3) What is Data Science and Who is a Data Scientist? None
4) Data Sources and Data Types None
5) Big Data Infrastructures: Hadoop and Spark None
6) Data Collection and Preprocessing None
7) Data Visualization Techniques None
8) Data Analysis with Python None
9) Introduction to Machine Learning None
10) Big Data and Artificial Intelligence None
11) Ethical, Privacy, and Security Issues None
12) Big Data Applications from Real Life None
13) Mini Project Presentations None
14) Trends in big data technologies None

Sources

Course Notes: Ders süresince eğitmen tarafından hazırlanacak haftalık ders slaytları, örnek veri setleri ve Python uygulama dosyaları öğrencilerle paylaşılacaktır. Ayrıca çevrim içi platformlar (Kaggle, Google Dataset Search) üzerinden destekleyici kaynaklara yönlendirme yapılacaktır. Marr, B. (2016). Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. Wiley. McKinney, W. (2022). Python for Data Analysis. O’Reilly.
References: Weekly lecture slides, sample datasets, and Python notebooks will be shared with students by the instructor. Supplementary resources will be introduced via online platforms such as Kaggle and Google Dataset Search. Marr, B. (2016). Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. Wiley. McKinney, W. (2022). Python for Data Analysis. O’Reilly.

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance 14 % 10
Laboratory % 0
Application % 0
Field Work % 0
Special Course Internship (Work Placement) % 0
Quizzes % 0
Homework Assignments 2 % 20
Presentation % 0
Project % 0
Seminar % 0
Midterms 1 % 30
Preliminary Jury % 0
Final 1 % 40
Paper Submission % 0
Jury % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 60
PERCENTAGE OF FINAL WORK % 40
Total % 100

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) Mastering the necessary concepts and theories in management, organization, marketing, finance, and human resources within businesses, and being able to make strategic decisions in these areas.
2) Effectively applying the knowledge acquired in business administration to real-life work situations and solving complex business problems.
3) Analyzing scientific research related to business administration, participating in such research, and developing innovative solutions.
4) Proficient in the use of computer software and digital tools utilized in business management, and effectively applying technology in data analysis.
5) Possessing the necessary communication and responsibility skills for both individual and group work, and demonstrating leadership in multidisciplinary and multicultural teams.
6) Adopting the need for businesses to operate according to ethical values, social responsibility, sustainability, and fair management in employer-employee-consumer relations.
7) Updating knowledge and skills in line with changing market conditions and having the competence to develop innovative and sustainable business strategies
8) Analyzing problems encountered in businesses with an analytical approach, developing strategic solutions, and ensuring the implementation of these solutions.
9) Updating knowledge and skills in line with changing market conditions and having the competence to develop innovative and sustainable business strategies.
10) Preparing, implementing, and evaluating sustainability-focused investment projects that provide both commercial success and benefit to society in a product or service business
11) Understanding the importance of employee benefits, opportunities, and work environment within businesses, and adopting a management approach based on equality, ethics, and sustainability in the workplace.