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  • Course Information
    • Course Title Code Semester Laboratory+Practice (Hour) Pool Type ECTS
    Data Mining and Text Analysis EBM502 FALL-SPRING 3+0 E 6
    Learning Outcomes
    1-Applies the most appropriate data mining method for a particular data set,
    2-Defines data mining processes,
    3-Applies data mining techniques to real world problems.
    4-Applies text analysis techniques.
  • ECTS / WORKLOAD
    • ActivityPercentage

    (100)

    		NumberTime (Hours)Total Workload (hours)
    Course Duration (Weeks x Course Hours)14342
    Classroom study (Pre-study, practice)14456
    Assignments0000
    Short-Term Exams (exam + preparation) 0000
    Midterm exams (exam + preparation)3011010
    Project3015050
    Laboratory 0000
    Final exam (exam + preparation) 4011010
    0000
    Total Workload (hours)   168
    Total Workload (hours) / 30 (s)     5,6 ---- (6)
    ECTS Credit   6
  • Course Content
    • Week Topics Study Metarials
    1 Data mining history R1-Part-1
    2 Data mining usage areas R1-Part-1
    3 Factors affecting data mining R1-Part-1
    4 Problems encountered in data mining: Redundant, Empty, Dynamic, Missing data R1-Part-2
    5 Problems encountered in data mining: Uncertainty, Limited information, Noisy and Missing values R1-Part-2
    6 Problems encountered in data mining: Different data types, Database size R1-Part-2
    7 Data mining process: Defining the problem - Preparing data R1-Part-5
    8 Data mining process: Establishment and evaluation of the model R1-Part-5
    9 Data mining process: Using and monitoring the model R1-Part-5
    10 Data mining methods: Classification and Regression R1-Part-7
    11 Data mining methods: Clustering R1-Part-7
    12 Data mining methods: Association rules R1-Part-7
    13 Text analysis techniques: Data acquisition and pre-processing R1-Part-8
    14 Text analysis techniques: Data analysis and visualization R1-Part-8
    Prerequisites -
    Language of Instruction Turkish
    Responsible Assist. Prof. Dr. Serkan SAVAŞ
    Instructors -
    Assistants -
    Resources -
    Supplementary Book 1 - Pektaş, A. O., (2013). SPSS ile veri madenciliği. Dikeyeksen Yayıncılık, İstanbul.
    Goals Introducing the basic concepts and techniques of Data Mining. Develop skills in using data mining techniques to solve practical problems. To gain the ability to apply text analysis techniques. Gaining experience of working independently and doing research.
    Content Data mining history, Data mining usage areas, Factors affecting data mining, Problems encountered in data mining: Redundant, Empty, Dynamic, Missing data, Problems encountered in data mining: Uncertainty, Limited information, Noisy and Missing values, Problems encountered in data mining: Different data types, Database size, Data mining process: Defining the problem - Preparing data, Data mining process: Establishment and evaluation of the model, Data mining process: Using and monitoring the model, Data mining methods: Classification and Regression, Data mining methods: Clustering, Data mining methods: Association rules, Text analysis techniques: Data acquisition and pre-processing, Text analysis techniques: Data analysis and visualization,
  • Program Learning Outcomes
    • Program Learning Outcomes Level of Contribution
    1 Acquires information by carrying out scientific research in the field of Electronics and Computer Engineering, evaluates the findings and makes comments 5
    2 Complements the restricted or incomplete information and applies it, unifies the multidisciplinary information 5
    3 Designs and implements a system meeting the requirements in the field of Electronics and Computer Engineering -
    4 Makes an interpretation of a problem in the field of Electronics and Computer Engineering, develops models for solutions and applies innovative methods in these solutions 3
    5 Has comprehensive knowledge on the contemporary applied method and techniques used in the field of Electronics and Computer Engineering and their limitations -
    6 Undertakes and implements analytic, simulation or experimental types of research and has the ability to solve the complex problems encountered there -
    7 Can participate and assume responsibility in multidisciplinary task forces -
    8 Observes the scientific, professional and ethical rules during data collection, its introduction and interpretation 5
    9 Be aware of recent advances and developments in the field of Electronics and Computer Engineering learns, analyses and applies them wherever needed 4
    10 Publishes his/her research findings verbally and in written forms in the national and international arena -
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