EMÜ737 - DATA MINING

Course Name Code Semester Theory
(hours/week)		 Application
(hours/week)		 Credit ECTS
DATA MINING EMÜ737 Any Semester/Year 3 0 3 10
Prequisites
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Question and Answer
Drill and Practice
Problem Solving
Other: Lecture, question and answer, problem solving, drill and practice, individual work  
Instructor (s)To be determined by the department  
Course objectiveDevelop skills to find patterns and regularities in massive data sets and extract useful knowledge from raw data 
Learning outcomes
  1. Define data types for variables
  2. Evaluate data quality and preprocess data
  3. Use classification methods and interpret the findings
  4. Use clustering methods and interpret the findings
  5. Follow the up-to-date data mining research and apply knowledge in scientific studies
  6. Use a data mining software and interpret the output of the analysis
Course ContentConcepts of data mining
Data preprocessing
Principal components analysis
Clustering
Classification
Prediction 
ReferencesShumeli, G., Patel, N.R., Bruce, P.C. (2012). Data Mining for Business Intelligence: Concepts, Techniques and Application in Microsoft Excel with XLMiner. E & B Plus.
Tan, P.N., Steinbach, M., Kumar, V. (2006). Introduction to Data Mining, Addison Wesley.
Larose, D.T. (2005). Discovering Knowledge in Data: An Introduction to Data Mining. Wiley Interscience. 

Course outline weekly

WeeksTopics
Week 1Overview of Data Mining
Week 2Types of Data/Data Quality
Week 3Data Preprocessing/Measures of Similarity
Week 4Exploring Data
Week 5Classification- Decision Trees
Week 6Classification- Decision Trees
Week 7Classification- Artificial Neural Network
Week 8Association Analysis
Week 9Midterm exam
Week 10Linear Regression
Week 11Linear Regression
Week 12Cluster Analysis
Week 13Cluster Analysis
Week 14Project Presentations and Discussions
Week 15Study for the Final Exam
Week 16Final Exam

Assesment methods

Course activitiesNumberPercentage
Attendance00
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments00
Presentation15
Project120
Seminar00
Midterms125
Final exam150
Total100
Percentage of semester activities contributing grade succes350
Percentage of final exam contributing grade succes150
Total100

WORKLOAD AND ECTS CALCULATION

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)1210120
Presentation / Seminar Preparation11010
Project15050
Homework assignment000
Midterms (Study duration)13030
Final Exam (Study duration) 14848
Total Workload30151300

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. Reach the necessary knowledge and methods required within the scope of industrial engineering through scientific research. Utilize these knowledge and methods upon evaluation and synthesis and implement them  X  
2. Follow the innovations, developments and literature on an international basis in the field of industrial engineering; have the competency to convert the research activities into scientific national and international publications and to contribute to the national and international science and technology literature.    X 
3. Perform a comprehesive analysis of the decision making problems; with a critical view evaluate the operations research and data based methodologies to model and solve these problems; implement after the synthesis or the development of these methods.    X
4. Perceive independently, design, plan, manage, monitor and conclude the research and development study process in the field of industrial engineering.   X 
5. Are aware of the academic responsbilities; describe the scientific, technological, economic, social, environmental and cultural impacts of the applications of Industrial Engineering; based on necessity, work individually or as a team member taking the scientific and institutional ethical values.  X  
6. Evaluate critically, report and present the results of the advanced research stuies and projects carried out in the field of industrial engineering  X  
7. Have the competency of the advanced use of software and information technologies required for Industrial Engineering    X
8. Design, model, develop and improve large scale systems.   X 
9. Raise the awareness of the decision makers through public quotation of the scientific, technological, social and cultural developments in the field of Industrial Engineering with a sense of scientific impartiality.  X  

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest