| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Big Data Analytics | BLM505 | Elective | Master's degree | 1 | Fall | 8 |
Name of Lecturer(s)
Assistant Prof. Dr. Fidan KAYA GÜLAĞIZ
Prof. Dr. Ahmet SAYAR
Assistant Prof. Dr. Hikmetcan ÖZCAN
Learning Outcomes of the Course Unit
1) Identify big data application areas
2) Use big data frameworks
3) Model and analyse data by applying selected techniques
4) Demonstrate an integrated approach to big data
5) Perceive core principles of data analytics
6) Understand realtime data stream analytics
7) Analyses social or spatial big data
8) Participate effectively in a team working with big data experts
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | ||
| Learning Outcomes | ||||||||||||
| 1 | No relation | No relation | No relation | No relation | No relation | No relation | Middle | No relation | High | No relation | No relation | |
| 2 | No relation | No relation | No relation | Low | No relation | Low | No relation | No relation | High | No relation | No relation | |
| 3 | No relation | No relation | Middle | Low | No relation | High | No relation | No relation | High | No relation | No relation | |
| 4 | No relation | No relation | No relation | Low | No relation | No relation | Low | No relation | High | No relation | No relation | |
| 5 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | High | No relation | No relation | |
| 6 | No relation | No relation | No relation | No relation | No relation | No relation | Low | No relation | High | No relation | No relation | |
| 7 | No relation | No relation | No relation | No relation | No relation | No relation | Middle | No relation | High | No relation | No relation | |
| 8 | No relation | No relation | No relation | No relation | No relation | No relation | Middle | No relation | High | No relation | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Mining of Massive Data Sets. Anand Rajaraman, Jure Leskovec, and Jeffrey D. Ullman. Cambridge University Press. 2011. Big Data. Principles and best practices of scalable realtime data systems. Nathan Marz and James Warren. April 2015; ISBN 9781617290343; 328 pages
Course Contents
Big data management topics include; scalable computing models, large-scale non-traditional data storage frameworks including graph, key-value, and column-family storage systems; data stream analysis; scalable prediction models and in-memory storage systems. Data analytics topics include; feature extraction and learning, ontology construction, similarity measures, dimension reduction, summary data structures, streaming, clustering in high dimensional space, frequent item sets, and mining social network graphs. In addition, content will also include Apache Spark and Hadoop big data frameworks and MapReduce absraction.
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
50% |
|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||
Contribution of Final Examination to Course Grade |
50% |
|||||||||||
Total | 100% | |||||||||||
Language of Instruction
Turkish
Work Placement(s)
Not Required