ECTS - Multi Dimensional Data Modeling
Multi Dimensional Data Modeling (ECON553) Course Detail
Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
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Multi Dimensional Data Modeling | ECON553 | Area Elective | 3 | 0 | 0 | 3 | 5 |
Pre-requisite Course(s) |
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N/A |
Course Language | English |
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Course Type | Elective Courses |
Course Level | Social Sciences Master's Degree |
Mode of Delivery | Face To Face |
Learning and Teaching Strategies | Lecture. |
Course Lecturer(s) |
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Course Objectives | The main aim of this course is to provide students with adequate knowledge in both programming in R software and theoretical multivariate statistical concepts. Hence, students will be able to use R in their multivariate statistical analysis related to their field of research. |
Course Learning Outcomes |
The students who succeeded in this course;
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Course Content | Multivariate statistics, factor analysis, principal component analysis, bootstrapping, state space analysis and Kalman Filter, Markov Chain Models, smooth transition, frequency domain, functional regression analysis. |
Weekly Subjects and Releated Preparation Studies
Week | Subjects | Preparation |
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1 | Some Concepts in Multivariate statistics | WWSW and KSS |
2 | Classification, Discrimination and Closeness | WWSW and KSS |
3 | Factor Analysis and Principal Component Analysis | WWSW and KSS |
4 | Bootstrapping | WWSW and KSS |
5 | State Space Analysis and Kalman Filter | WWSW, KSS and JDH |
6 | Markov Chain Models | WWSW, KSS and JDH |
7 | Smooth Transition and Threshold Models | Lecture notes available |
8 | Frequency Domain: Fourier Function | WWSW, KSS and JDH |
9 | Periodgram | WWSW |
10 | Asymptotic Concepts in N and T | JDH |
11 | Ridge regression and Lasso estimator | Lecture notes available |
12 | Functional Regression Analysis | Lecture notes available |
13 | Information Accumulated Multilayer Models (IAM) | Lecture notes available |
14 | Final Exam |
Sources
Course Book | 1. K. S. Srivastava (2002) Methods of Multivariate Statistics. Wiley Series in probability and statistics |
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2. W.W.S. Wei (1991) Time Series Analysis: Univariate and Multivariate Methods. Addison Wesley Publishing Company. | |
3. J. D. Hamilton (1994)Time Series Analysis. Princeton University Press |
Evaluation System
Requirements | Number | Percentage of Grade |
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Attendance/Participation | 14 | 10 |
Laboratory | - | - |
Application | - | - |
Field Work | - | - |
Special Course Internship | - | - |
Quizzes/Studio Critics | - | - |
Homework Assignments | - | - |
Presentation | 2 | 20 |
Project | - | - |
Report | - | - |
Seminar | - | - |
Midterms Exams/Midterms Jury | 1 | 20 |
Final Exam/Final Jury | 1 | 50 |
Toplam | 18 | 100 |
Percentage of Semester Work | |
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Percentage of Final Work | 100 |
Total | 100 |
Course Category
Core Courses | X |
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Major Area Courses | |
Supportive Courses | |
Media and Managment Skills Courses | |
Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
# | Program Qualifications / Competencies | Level of Contribution | ||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | ||
1 | To compare main microeconomic theories, approaches and make a critical evaluation of each | X | ||||
2 | To compare main macroeconomic theories, approaches and make a critical evaluation of each | X | ||||
3 | To apply mathematical modeling | X | ||||
4 | To employ statistical and econometric tools in analyzing an economic phenomena | X | ||||
5 | To analyze the main economic indicators and comment on them | X | ||||
6 | To acquire theoretical knowledge through literature survey and derive empirically confirmable hypothesis | X | ||||
7 | To make a research design and carry it out within predetermined time frames | X | ||||
8 | To be able to develop new approaches for complex problems in applied economics and/or apply statistical/econometric tools to new areas/problems | X | ||||
9 | To formulate and present policy recommendations based on academic research | X | ||||
10 | To combine economic knowledge with other disciplines in order to solve problems requiring scientific expertise | X | ||||
11 | To use information technology effectively | X | ||||
12 | To continue learning and undertake advanced research independently | X |
ECTS/Workload Table
Activities | Number | Duration (Hours) | Total Workload |
---|---|---|---|
Course Hours (Including Exam Week: 16 x Total Hours) | 14 | 3 | 42 |
Laboratory | |||
Application | |||
Special Course Internship | |||
Field Work | |||
Study Hours Out of Class | 14 | 3 | 42 |
Presentation/Seminar Prepration | 1 | 21 | 21 |
Project | |||
Report | |||
Homework Assignments | |||
Quizzes/Studio Critics | |||
Prepration of Midterm Exams/Midterm Jury | 1 | 20 | 20 |
Prepration of Final Exams/Final Jury | 1 | 25 | 25 |
Total Workload | 150 |