Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
DATA ANALYSIS AND MINING ABY212 4. Semester 0 + 0 0 5.0

Prerequisites None

Language of Instruction Turkish
Course Level Associate's Degree
Course Type Compulsory
Mode of delivery
Course Coordinator
Instructors
Assistants
Goals We will explain big data, properties of big data, data mining, automatic indexing and text mining, and we will make text and data mining application.
Course Content Big data, properties of big data, differences between big data and other data, data mining, information extraction, tokenization, computing frequencies, stemming, weighting, classification and clustering. Uses RapidMiner or Orange to gain skills in programme tools.
Learning Outcomes 1) Has knowledge about big data, data mining, automatic indexing and text mining terminology,
2) Has knowledge about data mining process and uses programme tools.
3) Has knowledge about computable text data and uses programme tools.
4) Has knowledge about statistical methods used in classification and clustering.
5) Uses tools of related programmes

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week 1-What is the data? Structured and unstructured data Lecture; Question Answer; Discussion
Opinion Pool; Workshop
Scenario Based Learning 		Presentation (Including Preparation Time)
2. Week 2-Big data, the characteristics of big data, internet of things Lecture; Question Answer; Discussion
Opinion Pool; Workshop
Scenario Based Learning 		Presentation (Including Preparation Time)
3. Week 3-Data Mining Lecture; Question Answer; Discussion
Opinion Pool; Workshop
Scenario Based Learning 		Presentation (Including Preparation Time)
4. Week 4-Classification Lecture; Question Answer; Discussion
Opinion Pool; Workshop
Scenario Based Learning 		Presentation (Including Preparation Time) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
5. Week 5-Classification Lecture; Question Answer; Discussion
Opinion Pool; Workshop
Scenario Based Learning 		Presentation (Including Preparation Time) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
6. Week 6-Classification Lecture; Question Answer; Discussion
Opinion Pool; Workshop
Scenario Based Learning 		Presentation (Including Preparation Time) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
7. Week 7-Clustering Lecture; Question Answer; Discussion
Opinion Pool; Workshop
Scenario Based Learning 		Presentation (Including Preparation Time) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
8. Week 8-Clustering Lecture; Question Answer; Discussion
Opinion Pool; Workshop
Scenario Based Learning 		Presentation (Including Preparation Time) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
9. Week 9-Training Case Study
Opinion Pool; Workshop
Scenario Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
10. Week 10-Text mining, releated terns and concepts, frequencies Lecture; Question Answer; Discussion
Opinion Pool; Workshop
Scenario Based Learning 		Presentation (Including Preparation Time) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
11. Week 11-Text mining, n-grams Lecture; Question Answer; Discussion
Opinion Pool; Workshop
Scenario Based Learning 		Presentation (Including Preparation Time) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
12. Week 12- Text mining, term weighting Lecture; Question Answer; Discussion
Opinion Pool; Workshop
Scenario Based Learning 		Presentation (Including Preparation Time) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
13. Week 13- Text mining, Simmilarity measurement Lecture; Question Answer; Discussion
Opinion Pool; Workshop
Scenario Based Learning 		Presentation (Including Preparation Time) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
14. Week 14- Training Case Study
Opinion Pool; Workshop
Scenario Based Learning 		Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)

Sources Used in This Course
Recommended Sources
"Dolgun, M. Özgür, Tülin Güzel Özdemir, Doruk Ouz. Veri madencilii’nde yapsal olmayan verinin analizi: Metin ve web madenciliği. İstatistikçiler Dergisi 2 (2009) 48-58 "
"Soy, Sue.(1998) Class Lecture Notes: H. P. Luhn and Automatic Indexing References to the Early Years of Automatic Indexing and Information Retrieval http://www.libsci.sc.edu/bob/confprog/confprog.htm "
Alpkoçak, A. (1995) Bilgi bulma sistemleri için otomatik Türkçe dizinleme yöntemi. Bilişim Konferansı Bildiriler 247-253
Çelik, Ufuk, Eyüp Akçetin, Murat Gök.(2017) Rapidminer ile uygulamalı veri madenciliği.Pusula.Ankara.
Dubin, D.(2004) The most influential paper Gerard Salton never wrote. Library Trends 52(4),748-764
Ekmekçioğlu, H.F, Lynch, H. F and Willet, P. Stemming and N-gram matching for term conflation in Turkish texts
Kao, B., Chi-Yuen Ng, L. And Cheung, D. (2000) Anchor point indexing in web document retrieval. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, 30(3), 364-373
Lee, D. L., Chuang, H and Seamons, K. (1997) Ranking and the vector space model. IEEE Software.(march/april), 67-76
Lovins, Julie Beth.(1968) Development of a Stemming Algorithm Mechanical Translation and Computational Linguistics (11; 1-2 ) 22-31
Raghavan, V.V, and Wong, S.K.M (1986) A Critical analysis of vector space model for information retrieval. JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE. 37(5):279-287
Salton, G, Wong, A and Yang, C.S. (1975) A Vector-space model for automatic indexing. Communications of the ACM 18(11), 613-701

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3DK4DK5
PY1555555
PY2555555
PY3555555
PY4555555

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 4
Work Hour outside Classroom (Preparation, strengthening) 14 10
Homework 2 10
Midterm Exam 1 1
Time to prepare for Midterm Exam 2 15
Final Exam 1 1.5
Time to prepare for Final Exam 2 15
2 4
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload