Covering the use of existing technologies, tools, architectures and systems in Big Data, it covers analytical data generation, storage, management, transfer, in-depth analysis of incoming big data.
Prerequisite(s)
MEP4000 BIG DATA
Corequisite(s)
MEP4000 BIG DATA
Special Requisite(s)
The minimum qualifications that are expected from the students who want to attend the course.(Examples: Foreign language level, attendance, known theoretical pre-qualifications, etc.)
Instructor(s)
Assist. Prof. Dr..Gökhan Kırbaç
Course Assistant(s)
Schedule
THURSDAY, 12:00-14:50, 2A01 İncirli Campus
Office Hour(s)
THURSDAY, 12:00-14:50, 2A01 İncirli Campus
Teaching Methods and Techniques
-lecture, discussion,
Principle Sources
-Big Data Fundamentals: Concepts, Drivers & Techniques (1st ed.). Thomas Erl, Wajid Khattak, and Paul Buhler. Prentice Hall Press, Upper Saddle River, NJ, USA. 2016.
2. Big Data, Principles and Best Practices of Scalable Realtime Data Systems, Nathan Marz and James Warren, Manning Publications 2015.
Other Sources
-Big Data Fundamentals: Concepts, Drivers & Techniques (1st ed.). Thomas Erl, Wajid Khattak, and Paul Buhler. Prentice Hall Press, Upper Saddle River, NJ, USA. 2016.
2. Big Data, Principles and Best Practices of Scalable Realtime Data Systems, Nathan Marz and James Warren, Manning Publications 2015.
Course Schedules
Week
Contents
Learning Methods
1. Week
Introduction to Big Data
Verbal lecture
2. Week
Big Data Storage and Analysis
Verbal lecture
3. Week
Big Data Analysis Techniques
Verbal lecture
4. Week
Big Data Analysis Techniques
Verbal lecture
5. Week
Visualization in Big Data Sets
Verbal lecture
6. Week
Visualization in Big Data Sets
Verbal lecture
7. Week
Advanced topics and applications in big data.
Verbal lecture
8. Week
MIDTERM EXAM
MIDTERM EXAM
9. Week
Advanced topics and applications in big data.
Verbal lecture
10. Week
Advanced topics and applications in big data.
Verbal lecture
11. Week
Advanced topics and applications in big data.
Verbal lecture
12. Week
Advanced topics and applications in big data.
Verbal lecture
13. Week
Advanced topics and applications in big data.
Verbal lecture
14. Week
Advanced topics and applications in big data.
Verbal lecture
15. Week
FINAL EXAM
FINAL EXAM
16. Week
FINAL EXAM
FINAL EXAM
17. Week
FINAL EXAM
FINAL EXAM
Assessments
Evaluation tools
Quantity
Weight(%)
Homework / Term Projects / Presentations
1
40
Final Exam
1
60
Program Outcomes
PO-1
Learning the basic concepts of Electronic Commerce
PO-2
To be able to analyze the relationship between Electronic Commerce and Internet correctly
PO-3
To fully grasp the legal dimension of Electronic Commerce
Learning Outcomes
LO-1
Develop an understanding of the concept of big data
LO-2
Developing data-based analytical thinking skills
LO-3
LEARNING to use big data tools and techniques
LO-4
LEARNING how big data works when developing business strategy
LO-5
Understanding the effects of big data on business life
