| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Big Data | YZM413 | Elective | Bachelor's degree | 4 | Fall | 5 |
Name of Lecturer(s)
Assistant Prof. Dr. Yasemin GÜLTEPE
Assistant Prof. Dr. Kaplan KAPLAN
Learning Outcomes of the Course Unit
1) Learn how to process very large data sets
2) Learns big data processing and mining techniques
3) Understands, constructs and uses different frameworks of big data
4) Understands the applicability and use of large data frameworks that flow and run in the background in solving real problems.
5) Defines big data usage scenarios in industry
6) Understands how suggestion and estimation systems can be improved
7) Hadoop and Spark see how to set up and use big data frameworks as clustering and distributed computing architectures
Program Competencies-Learning Outcomes Relation
| Program Competencies | |||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | ||
| Learning Outcomes | |||||||||||||
| 1 | No relation | High | Middle | High | Low | Low | Middle | High | Middle | Low | Low | Low | |
| 2 | High | High | Middle | Middle | Low | Low | Middle | Low | Low | Low | Low | Low | |
| 3 | High | High | Middle | Middle | Middle | Low | Middle | Low | Low | Low | Low | Low | |
| 4 | High | High | Middle | High | Low | No relation | Middle | High | Middle | Low | Middle | Low | |
| 5 | High | Middle | Low | High | Low | Low | Middle | Low | High | Middle | Middle | Low | |
| 6 | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Low | High | Middle | Middle | Low | |
| 7 | High | High | High | Middle | Middle | Low | Middle | Low | High | Middle | Middle | Low | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Not Required
Course Contents
Advances in web technologies and the increase in the creation, collection and use of data have led to an incredible increase in the size and complexity of data in many data-driven applications. Big data is difficult to manage for 3 main reasons: larger data volume, more complex data and faster data change. In this age of big data, the increase in data size and complexity has triggered the development of new data analysis algorithms and technologies that are scalable, open to interaction, and can process complex and dynamic data. This course will mainly focus on text processing algorithms and programming using big data frameworks and technologies. In order to gain better computing power and make operations faster, programming models will be developed to distribute operations to distributed computing clusters, or existing technologies will be used.
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Language of Instruction
Turkish
Work Placement(s)
Not Required