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
Multi Dimensional Data Modeling ECON553 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language Turkish
Course Type N/A
Course Level Social Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
  • Prof. Dr. Tolga Omay
Course Assistants
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;
  • Upon the completion of this course, the student will be able to: Comprehend knowledge in working with R software for multivariate statistical analysis.
  • Interpret the results of the research according to the multivariate statistical methods applied to data.
  • Utilize the R for describing and analyzing the quantitative data.
  • Understand and apply mathematical concepts and reasoning, analyze and interpret various types of data.
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
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
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
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
Percentage of Final Work 100
Total 100

Course Category

Core Courses X
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
2 To compare main macroeconomic theories, approaches and make a critical evaluation of each
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
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