TYPE OF COURSE UNIT | Elective Course |
LEVEL OF COURSE UNIT | Master's Degree Without Thesis |
YEAR OF STUDY | - |
SEMESTER | - |
NUMBER OF ECTS CREDITS ALLOCATED | 10 |
NAME OF LECTURER(S) | -
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LEARNING OUTCOMES OF THE COURSE UNIT |
At the end of this course, the students; 1) will be able to explain the theory of the basic concepts of data mining 2) will be able to prepare the data warehouse from operational database(s) 3) will be able to explain the data mining tasks 4) will be able to explain the components of data mining 5) will be able to explain the relationship between decision processes and business intelligence 6) will be able to differentiate data mining methods from the other analytical methods and business intelligence solutions 7) will be able to prepare data mining projects according to the CRISP-DM and the other data mining methodologies 8) will be able to identify the proper data mining method according to the data and the goals 9) will be able to explain the statistical infrastructure of data mining
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MODE OF DELIVERY | Face to face |
PRE-REQUISITES OF THE COURSE | No |
RECOMMENDED OPTIONAL PROGRAMME COMPONENT | None |
COURSE DEFINITION | This course provides students with a general knoıwledge on data mining techniques, and analyzing data sets and commenting on the results. |
COURSE CONTENTS | WEEK | TOPICS |
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1st Week | Introduction to data mining | 2nd Week | Data mining tasks | 3rd Week | Data mining tasks | 4th Week | Components of data mining | 5th Week | Components of data mining | 6th Week | Comparison of data mining and the other analytical methods | 7th Week | Comparison of data mining and the other analytical methods | 8th Week | Midterm | 9th Week | Decision processes, business intelligence and data mining | 10th Week | Methodology of data mining | 11th Week | Classification of data mining methods | 12th Week | Statistical infrastructure of data mining | 13th Week | Data mining methods and their applications | 14th Week | Data mining methods and their applications |
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RECOMENDED OR REQUIRED READING | 1. Hand, David, Mannila, Heikki and Smyth, Padhraic. Principles of Data Mining. MIT Press, London, 2001. 2. Hastie, Trevor, Tibshirani, Robert and Friedman, Jerome. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Second Edition). Springer Series in Statistics, 2009. |
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS | Presentation,Experiment,Lecture,Problem Solving,Practice |
ASSESSMENT METHODS AND CRITERIA | | Quantity | Percentage(%) |
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Mid-term | 1 | 25 | Practice | 1 | 20 | Project | 1 | 10 | Total(%) | | 55 | Contribution of In-term Studies to Overall Grade(%) | | 55 | Contribution of Final Examination to Overall Grade(%) | | 45 | Total(%) | | 100 |
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ECTS WORKLOAD |
Activities |
Number |
Hours |
Workload |
Midterm exam | 1 | 3 | 3 | Preparation for Quiz | | | | Individual or group work | 14 | 10 | 140 | Preparation for Final exam | 1 | 45 | 45 | Course hours | 14 | 3 | 42 | Preparation for Midterm exam | 1 | 30 | 30 | Laboratory (including preparation) | | | | Final exam | 1 | 3 | 3 | Homework | 1 | 40 | 40 | Total Workload | | | 303 |
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Total Workload / 30 | | | 10,1 |
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ECTS Credits of the Course | | | 10 |
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LANGUAGE OF INSTRUCTION | Turkish |
WORK PLACEMENT(S) | No |
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