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Course Description Course Name : Data Mining Methods II Course Code : IEM 756 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Spring (16 Weeks) ECTS : 6 Name of Lecturer(s) : Assoc.Prof.Dr. S.BİLGİN KILIÇ Learning Outcomes of the Course : Gains the ability to produce useful information by means of discovering the patterns, basic relationships, interactions, changes, irregularities, rules, and statistically significant structures in the raw data Gains the ability to perform parametric and nonparametric anlysis using computer Gains the ability to think analytically Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : Data mining course aims to produce useful information by means of discovering the patterns, basic relationships, interactions, changes, irregularities, rules, and statistically significant structures in the data Course Contents : The course covers remembering basic data mining methods and statistical concepts, the basic features of SPSS, definition of variables and calculation of summary descriptive statistics in SPSS, discovering the basic interactions and relationships between the variables and dimensionality reduction methods; principal components factor analysis, nonparametric methods; artificial neural networks method, parametric methods; logit analysis, discriminant analysis, cluster analysis, hierarchical cluster analysis, k-piece grouping method Language of Instruction : Turkish Work Place : Classroom, Compurer Labrotary   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Remembering basic data mining methods and statistical concepts Reading relevant parts in the source books according to the weekly program Lecture and computer application in the labrotory 2 Discovering the basic interactions and relationships between the variables and dimensionality reduction methods; principal components factor analysis Reading relevant parts in the source books according to the weekly program Lecture and computer application in the labrotory 3 Principal components factor analysis; continued Reading relevant parts in the source books according to the weekly program Lecture and computer application in the labrotory 4 Nonparametric methods: Artificial neural networks method, Reading relevant parts in the source books according to the weekly program Lecture and computer application in the labrotory 5 Artificial neural networks method; continued Reading relevant parts in the source books according to the weekly program Lecture and computer application in the labrotory 6 Parametric methods:Logit analysis Reading relevant parts in the source books according to the weekly program Lecture and computer application in the labrotory 7 Logit analysis; continued Reading relevant parts in the source books according to the weekly program Lecture and computer application in the labrotory 8 Midterm exam 9 Discriminant analysis Reading relevant parts in the source books according to the weekly program Lecture and computer application in the labrotory 10 Discriminant analysis; continued Reading relevant parts in the source books according to the weekly program Lecture and computer application in the labrotory 11 Hierarchical cluster analysis Reading relevant parts in the source books according to the weekly program Lecture and computer application in the labrotory 12 Hierarchical cluster analysis; contiued Reading relevant parts in the source books according to the weekly program Lecture and computer application in the labrotory 13 k-nearest neighbor algorithm Reading relevant parts in the source books according to the weekly program Lecture and computer application in the labrotory 14 Decision tree classification algorithm Reading relevant parts in the source books according to the weekly program Lecture and computer application in the labrotory 15 c4.5 algorithm Reading relevant parts in the source books according to the weekly program Lecture and computer application in the labrotory 16/17 Final exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Veri Madenciliği: Kavram ve Algoritmaları Doç, Dr. Gökhan SİLAHTAROĞLU  Veri Madenciliği (Kavram ve Teknikler) Aysan Şentürk Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 80     Homeworks/Projects/Others 10 20 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Explains Econometric concepts 3 2 Equipped with the foundations of Economics, develops Economic models 3 3 Models problems using the knowledge of Mathematics, Statistics, and Econometrics 4 4 Acquires the ability to analyze, benchmark, evaluate and interpret at conceptual levels to develop solutions to problems 5 5 Collects, edits, and analyzes data 5 6 Uses advanced software packages concerning Econometrics, Statistics, and Operation Research 5 7 Develops the ability to use different resources in an area which has not been studied in the scope of academic rules, synthesizes the information gathered, and gives effective presentations 5 8 Speaks Turkish and at least one other foreign language in accordance with the requirements of academic and business life. 3 9 Questions traditional approaches and their implementation and develops alternative study programs when required 4 10 Recognizes and implements social, scientific, and professional ethic values 4 11 Gives a consistent estimate for the model and analyzes and interprets its results 5 12 Takes responsibility individually and/or as a member of a team; leads a team and works effectively 3 13 Defines the concepts of statistics, operations research and mathematics. 5 14 Knowing the necessity of life-long learning, follows the latest developments in the field of study and improves himself continiously 3 15 Follows the current issues, and interprets the data about economic and social events. 3 16 Understands and interprets the feelings, thoughts and behaviours of people and expresses himself/herself orally and in written form efficiently 3 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 3 42     Out of Class Study (Preliminary Work, Practice) 14 3 42 Assesment Related Works     Homeworks, Projects, Others 10 6 60     Mid-term Exams (Written, Oral, etc.) 1 2 2     Final Exam 1 2 2 Total Workload: 148 Total Workload / 25 (h): 5.92 ECTS Credit: 6