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COURSE UNIT TITLECOURSE UNIT CODESEMESTERTHEORY + PRACTICE (Hour)ECTS
BIG DATA ANALYSIS EMBA509 - 3 + 0 10

TYPE OF COURSE UNITElective Course
LEVEL OF COURSE UNITMaster's Degree Without Thesis
YEAR OF STUDY-
SEMESTER-
NUMBER OF ECTS CREDITS ALLOCATED10
NAME OF LECTURER(S)Professor Hakkı Okan Yeloğlu
LEARNING OUTCOMES OF THE COURSE UNIT At the end of this course, the students;
1) Will have broad knowledge about data management
2) Are able to scan large data chunks and extract meaningful information patterns from them
3) Will learn the ethical principles that must be followed in the data collection process.
4) Will have knowledge of the algorithms used in the large data collection process.
MODE OF DELIVERYFace to face
PRE-REQUISITES OF THE COURSENo
RECOMMENDED OPTIONAL PROGRAMME COMPONENTnone
COURSE DEFINITIONAt the end of this course students will be able to make analysis from complex and dense data sets. Also, they will be able to uncover hidden patterns and gather meaning information from hidden patterns in datasets. In this course, students will learn different data analysis methods, analytical modeling, basic optimization and statistical techniques. In this regard, this course will cover the following topics: basic data management, data mining, social network analysis, machine learning, mathematical decision models and statistical analysis.
COURSE CONTENTS
WEEKTOPICS
1st Week Basic data management
2nd Week Basic data management
3rd Week Data mining
4th Week Data mining
5th Week Data mining
6th Week Social network analysis
7th Week Social network analysis
8th Week Midterm
9th Week Machine learning
10th Week Machine learning
11th Week Mathematical decision models
12th Week Mathematical decision models
13th Week Statistical analysis
14th Week Statistical analysis
15th Week
RECOMENDED OR REQUIRED READINGLecture notes
PLANNED LEARNING ACTIVITIES AND TEACHING METHODSLecture,Discussion,Case Study,Practice
ASSESSMENT METHODS AND CRITERIA
 QuantityPercentage(%)
Mid-term140
Assignment110
Quiz110
Total(%)60
Contribution of In-term Studies to Overall Grade(%)60
Contribution of Final Examination to Overall Grade(%)40
Total(%)100
ECTS WORKLOAD
Activities Number Hours Workload
Midterm exam111
Preparation for Quiz
Individual or group work148112
Preparation for Final exam13939
Course hours14342
Preparation for Midterm exam14040
Laboratory (including preparation)
Final exam111
Homework51155
Quiz11010
Total Workload300
Total Workload / 3010
ECTS Credits of the Course10
LANGUAGE OF INSTRUCTIONTurkish
WORK PLACEMENT(S)No
  

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K16