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Social Network Analysis and Data Mining

Course Code: 
BTSM 502
Semester: 
Bahar
Course Type: 
Zorunlu
P: 
3
Lab: 
0
Credits: 
3
ECTS: 
10
Course Objectives: 
Data mining is the process of reaching information among large-scale data, capturing hidden patterns. We can make predictions about the future from large data, it is a process that enables to reveal the factors forming the associations, similarities and differences. The aim of this course is to grasp the aims of data mining methods and to analyze them with data mining software
Course Content: 

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

Course Methodology: 
1. Lecture 3. Discussion 4. Demonstration 5. Group work 6. Microteaching 7. Problem solving
Course Evaluation Methods: 
A. Supply type B. Multiple-choice test C. Incomplete D. True-False E. Oral exam F. Portfolio G. Performance type H. Report

Vertical Tabs

Dersin Öğrenme Çıktıları

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

Dersin Akışı

COURSE CONTENT
Week Topics Study Materials
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

Kaynaklar

RESOURCES
Compulsory 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

Recommended 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

Materyal Paylaşımı

MATERIAL SHARING
Documents  
Assignments 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

Exams  

Değerlendirme Sistemi

ASSESSMENT
IN-TERM STUDIES Quantity Percentage
Midterm 1 30
Quiz    
Final 1 40
Assignment 1 30
Total   100
Contribution of Final Exam to Overall Grade   40
Contribution of In-term Studies to Overall Grade   60
Total   100

Dersin Program Çıktılarına Katkısı

COURSE'S CONTRIBUTION TO PROGRAM
No Program Learning Outcomes Contribution
1 2 3 4 5  
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          

AKTS İş Yükü Tablosu

 
ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities Quantity Duration
(Hour)
Total
Workload
(Hour)
Course Duration (Including theexamweek: 16x Total coursehours) 16 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 4 64
Mid-Term(s) 1 12 12
Quiz(es) - - -
Assignment(s) 1 12 12
Final 1 20 20
Total Work Load     156
Total Work Load / 25 (h)     6,24
ECTS Credit of the Course     6