YBS610


Course Title Course Code Program Level
BIG DATA AND HEALTH INFORMATION APPLICATIONS YBS610 Management Information Systems (Doctorate) M.A / M.Sc.

Course Term
(Course Semester)
Teaching and Learning Methods
Credits
Theory Practice Lab Projects/Field Work Seminars/Workshops Other Total Credits ECTS Credits
02
(Spring)
186 3 8

Teaching Staff
Language of Instruction Türkçe (Turkish)
Type Of Course Elective
Prerequisites
Recommended Optional Programme Component
Course Objectives The aim of the course is to teach the application of data mining methods on data in the field of health and the application of big data analytics algorithms on big data in the field of health.
Course Content Data mining, MapReduce, finding similar elements, distance measures, data stream mining, frequent itemsets, clustering, data mining in big data in healthcare, big data analytics in healthcare
Learning Outcomes (LO) At the end of this course, the student will gain the following characteristics; 1. Knows the application areas of data mining methods. 2. Knows the comparison of data mining methods and evaluation of the results. 3. Knows how to classify data based on distance criteria. 4. Knows how to cluster data based on distance criteria. 5. Knows the characteristics of big data. 6. Knows how to apply big data analytics algorithms on big data.
Mode of Delivery Face to face
Course Outline
Week Topics
1. Week Veri madenciliği
2. Week MapReduce
3. Week Finding similar elements
4. Week Distance metrics
5. Week Classification algorithms
6. Week Application of classification algorithms in healthcare
7. Week Midterm
8. Week Data stream mining
9. Week Clustering algorithms
10. Week Application of clustering algorithms in healthcare
11. Week Frequent itemsets and their application in health data
12. Week Data mining in big data in healthcare
13. Week Big data analytics in healthcare
14. Week End of term exam
Assessment
  Percentage(%)
Mid-term (%) 20
Quizes (%)
Homeworks/Term papers (%) 20
Practice (%) 10
Labs (%)
Projects/Field Work (%) 10
Seminars/Workshops (%)
Final (%) 40
Other (%)
Total(%) 100
Course Book (s) and/or References Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Stanford University, 2011. (1) Real-Time Big Data Analytics: Emerging Architecture, Mike Barlow, O’Reilly Media, 2013. (2) Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners, Jared Dean, Wiley, 2014. (3) Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, EMC Education Services, 2015.
Work Placement(s)
The Relationship between Program Qualifications (PQ) and Course Learning Outcomes (LO)