BIG DATA ANALYTICS AND MANAGEMENT (ENGLISH, NONTHESIS)


Profile of the Program

In today's interconnected world, data is being generated at an unprecedented scale, from social media activity and e-commerce transactions to sensor data from smart devices and real-time streaming data. This constant flow of information presents both a challenge and an opportunity. The ability to make sense of this vast array of data is crucial not only for business intelligence but also for societal advancement. The program emphasizes the need for a multidisciplinary approach, preparing graduates to apply their expertise across a wide range of fields—from healthcare and finance to environmental science, entertainment, and public policy. The Big Data Analytics and Management graduate program is designed to equip students with the knowledge and skills required to harness the power of big data in an increasingly data-driven world. As organizations across industries face an overwhelming amount of data, the ability to collect, manage, and analyze this information has become a critical factor for success. The program focuses on the essential pillars of big data: data collection, data analytics, and data management, with an emphasis on transforming raw data into meaningful insights that drive decision-making. Big data is not limited to a single industry or domain. It spans virtually every sector, and the program is designed to reflect this diversity. Whether it's predicting disease outbreaks, optimizing supply chains, enhancing customer experiences, or driving innovation in smart cities, big data plays a central role. As industries recognize the potential of big data to unlock new opportunities, the demand for skilled professionals who can understand and manipulate complex datasets continues to rise. Our program focuses on developing not just technical proficiency but also a strategic understanding of how data can be used to solve real-world problems. Graduates will leave equipped with the tools necessary to manage large datasets, apply advanced analytical techniques, and communicate insights to stakeholders across diverse organizational settings. The ever-evolving nature of big data means that our curriculum emphasizes adaptability and continuous learning, preparing students to thrive in an industry defined by rapid change and innovation.

Qualification Awarded

The students who successfully complete the program are awarded the degree of Bachelor of The Big Data Analytics and Management

Level of Qualification

This is a program.

Specific Admission Requirements

In accepting students to non-thesis master's programs, a bachelor's degree or equivalent graduation certificate/diploma, a foreign language proficiency certificate for programs conducted in English or another foreign language, and other criteria recommended by the Institute's Board of Directors and determined by the Senate are required.

Qualification Requirements and Regulations

The students studying in this graduate program are required to have a Cumulative Grade Points Average (Cum.GPA) of not less than 3.00/4.00 and have completed all the courses with at least a letter grade of C/S in the program in order to graduate. The minimum number of ECTS credits required for graduation is 90. It is also mandatory for the students to complete their compulsory project work in a specified duration and quality.

Recognition of Prior Learning

Students who will continue their education at Bahçeşehir University may be exempted from the courses they took at their previous educational institution within the framework of certain regulations. If the content of the course taken is compatible with the content of the course given at BAU and is approved by the institute directorate, the student may be exempted from this course.

Occupational Profiles of Graduates

The program emphasizes the need for a multidisciplinary approach, preparing graduates to apply their expertise in a wide range of fields, from health and finance to environmental science, entertainment and public policy.

Access to Further Studies

Those who registered for the Non-Thesis Master's program before 06/02/2013 or those who have already graduated can apply to doctoral programs. Those who registered for the Non-Thesis Master's program after 06/02/2013 cannot apply to doctoral programs.

Program Outcomes

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

Course & Program Outcomes Matrix

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes 1 2 3 4 5 6 7
Courses
BDA5001 Introduction to Big Data 3 3 3 2 4 1 5
BDA5002 Marketing Analytics 3 2 4 3 3 2 3
BUS5301 Research Methods and Ethics 5 3 2 2 2 2 3
SEN5999 Project 5 5 5 5 5 5 5
Departmental Elective
BDA5010 Big Data and Hadoop Environment
BDA5011 Big Data and Analytics
BDA5012 Big Data in Cloud Computing
BDA5015 Exploratory Data Analytics and Visualization
BDA5121 Enterpreneurship and Managing Big Data
CMP5103 Artificial Intelligence
CMP5121 Network Security and Cryptography
CMP5123 Computer Networks and Mobile Communications
CMP5126 Image and Video Processing
CMP5130 Machine Learning and Pattern Recognition
CMP5133 Artificial Neural Networks
CMP5151 Software Design Patterns
CMP5203 High Performance Computer Architecture
CMP5208 Parallel Computing with GPUs
CMP5550 Computer Vision
CMP5931 Special Topics I
CMP6138 Analysis of Algorithms
ENM5203 Statistical Data Analysis and Decision Making
INE5110 Probabilistic Models and Applications
INE5111 Mathematical Programming and Modelling
INE5261 Multi Attribute Decision Making
INE6105 Stochastic Models
MAT5101 Engineering Mathematics
SEN5104 Advanced System Analysis and Design II
SEN5144 Software Project Management
SEN5301 Introduction to Information Technologies Services Management
SEN5315 Service Oriented Architectures
SEN5604 Information Security Management 2 2 1 1 1 1 1
GE-Elective

Course Structure Diagram with Credits

1. Semester
Course Code Course Name Prerequisites Theoretical Practical Credit ECTS
BDA5001 Introduction to Big Data 3 0 3 8
BUS5301 Research Methods and Ethics 3 0 3 9
Departmental Elective 3 6
Departmental Elective 3 7
Total 30
2. Semester
Course Code Course Name Prerequisites Theoretical Practical Credit ECTS
BDA5002 Marketing Analytics 3 0 3 8
Departmental Elective 3 8
Departmental Elective 3 7
Departmental Elective 3 7
Total 30
3. Semester
Course Code Course Name Prerequisites Theoretical Practical Credit ECTS
SEN5999 Project 0 0 0 16
Departmental Elective 3 7
Departmental Elective 3 7
Total 30

Program Director (or Equivalent)


BIG DATA ANALYTICS AND MANAGEMENT (ENGLISH, NONTHESIS) - GRADUATE SCHOOL