| Teaching Staff
|
PROF. DR. ALAATTİN PARLAKKILIÇ |
| Language of Instruction |
Türkçe (Turkish) |
| Type Of Course |
Compulsory |
| 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 (%) |
40 |
| Quizes (%) |
|
| Homeworks/Term papers (%) |
|
| Practice (%) |
|
| Labs (%) |
|
| Projects/Field Work (%) |
|
| Seminars/Workshops (%) |
|
| Final (%) |
60 |
| 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) |
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| The Relationship between Program Qualifications (PQ) and Course Learning Outcomes (LO) |
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