Data Mining Concepts, Data Pre-Processing Processes, Statistical Learning Theory (Naive Bayes), Clustering Methods (K-Means, X-Means, Hierarchical), Decision Trees and Decision Rules, Association Rules, Text Mining, Social Network Analysis
|Learning Outcomes||Teaching Methods||Assessment Methods|
|1) data mining concepts and exemplifies usage areas.||1, 2||F, H|
|2) the importance of data pre-processing, counts pre-processing steps, preprocessing steps||1, 2, 3||F, H|
|3) the difference between supervised and unsupervised learning methods.||2, 4, 7||F, H|
|4) the Naive Bayes method, a classification example.||2, 4, 7||A, F, G|
|5) Classification with Decision Trees.||2, 4, 7||A, F, G|
|6) Simulating the recommendation system with association rules||11, 4||2, 4, 7|
|7) Applying sentiment analysis of social media messages by using text mining||1, 3||A|
|8) Applying social network analysis methods to social media messages.||11, 4||2, 4, 5|
|1||Basic concepts||Investigation of concepts|
|2||Data Pre-Processing Processes||Investigation of pre-processing processes, Book Chapter: Related book chapter|
|3||Data Mining Methods||Researching counseling and counseling|
|4||Decision Trees||Related book chapter and articles|
|5||KNN Algorithm and Random Forest||Related book chapter and articles|
|6||Naive Bayes and Artificial Neural Networks||Related book chapter and articles|
|7||RVM and SVM||Related book chapter and articles|
|8||K-Medoid and K-Means||Related book chapter and articles|
|9||Fuzzy c-Means and Kohenen Map||Related book chapter and articles|
|10||Text Mining||Research on text similarity, working principles of plagiarism software|
|11||Sentiment Analysis||Current articles on emotion analysis|
|12||Basic Graph Knowledge and Network Concepts||Current articles on social network analysis|
|13||Network Models and Community Analysis||Current articles on social network analysis|
|14||Network Analysis and Network Visualization||Current articles on social network analysis|
1) Gravetter, F.J. and Wallnau, L.B. (2012). Statistics for the Behavioral Sciences.9th edition. Wadsworth, USA.
2) Pallant, J. (2011). SPSS Survival Manual: A Step by Step Guide to data Analysis using SPSS,4th edition. Open University Press, USA
1) Salkind, N.J. (2011). Statistics for People Who (Think They) Hate Statistics, 4th Edition, Sage, USA.
2) Heiman, G.W. (2011). Basic Statistics for the Behavioral Sciences. 6th edition. Wadsworth, USA
1) The students can reach any data related with their interests.
2) Applies the data preprocessing processes to the gathered data.
3) Create a prediction model using classification methods and strengthen the mode by applying performance evaluation measures.
4) Discuss similar items in the data using clustering methods.
5) Analyzes the sentiment of Twitter messages.
6) Visualize Twitter messages by applying social network analysis
|Contribution of Final Exam to Overall Grade||40|
|Contribution of In-term Studies to Overall Grade||60|
|COURSE'S CONTRIBUTION TO PROGRAM|
|No||Program Learning Outcomes||Contribution|
|1||To know the variables of learning teaching process and make appropriate designs.||X|
|2||To do qualitative and quantitative scientific research methods, to research in the field and present the results in written form.||X|
|3||Know how to use the internet and technology for accessing information, sharing information, professional development and data analysis||X|
|4||To examine the cognitive and psychological connections on social media||X|
|5||To have the qualifications required by the profession and to have the necessary skills to develop them constantly.||X|
|6||To be able to evaluate using scientific principles and techniques.||X|
|7||To be aware of social, cultural and social responsibilities and to use them appropriately in their field.||X|
|8||To develop appropriate projects and to have control over project management processes.||X|
|ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION|
|Course Duration (Including theexamweek: 16x Total coursehours)||16||3||48|
|Hours for off-the-classroom study (Pre-study, practice)||16||4||64|
|Total Work Load||156|
|Total Work Load / 25 (h)||6,24|
|ECTS Credit of the Course||6|