ECTS - Multi Dimensional Data Modeling
Multi Dimensional Data Modeling (ECON482) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Multi Dimensional Data Modeling | ECON482 | Area Elective | 3 | 0 | 0 | 3 | 6 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture. |
| Course Lecturer(s) |
|
| 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;
|
| 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 | Midterm Exam | |
| 7 | Markov Chain Models | WWSW, KSS and JDH |
| 8 | Smooth Transition and Threshold Models | Lecture notes available |
| 9 | Frequency Domain: Fourier Function | WWSW, KSS and JDH |
| 10 | Periodgram | WWSW |
| 11 | Asymptotic Concepts in N and T | JDH |
| 12 | Ridge Regresyonu ve Lasso Tahmincisi | Lecture notes available |
| 13 | Functional Regression Analysis | Lecture notes available |
| 14 | Information Accumulated Multilayer Models (IAM) | Lecture notes available |
| 15 | Information Accumulated Multilayer Models (IAM) | Lecture notes available |
| 16 | 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 | 1 | 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 | 5 | 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 | Acquires skills to use the advanced theoretical and applied knowledge obtained at the mathematics bachelors program to do further academic and scientific research in both mathematics-based graduate programs and public or private sectors. | |||||
| 2 | Transplants and applies the theoretical and applicable knowledge gained in their field to the secondary education by using suitable tools and devices. | |||||
| 3 | Acquires the skill of choosing, using and improving problem solving techniques which are needed for modeling and solving current problems in mathematics or related fields by using the obtained knowledge and skills. | |||||
| 4 | Acquires analytical thinking and uses time effectively in the process of deduction. | |||||
| 5 | Acquires basic software knowledge necessary to work in the computer science related fields and together with the skills to use information technologies effectively. | |||||
| 6 | Obtains the ability to collect data, to analyze, interpret and use statistical methods necessary in decision making processes. | |||||
| 7 | Acquires the level of knowledge to be able to work in the mathematics and related fields and keeps professional knowledge and skills up-to-date with awareness in the importance of lifelong learning. | |||||
| 8 | Takes responsibility in mathematics related areas and has the ability to work affectively either individually or as a member of a team. | |||||
| 9 | Has proficiency in English language and has the ability to communicate with colleagues and to follow the innovations in mathematics and related fields. | |||||
| 10 | Has the ability to communicate ideas with peers supported by qualitative and quantitative data. | |||||
| 11 | Has professional and ethical consciousness and responsibility which takes into account the universal and social dimensions in the process of data collection, interpretation, implementation and declaration of results in mathematics and its applications. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 3 | 48 |
| Presentation/Seminar Prepration | 1 | 10 | 10 |
| Project | |||
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 10 | 10 |
| Prepration of Final Exams/Final Jury | 1 | 25 | 25 |
| Total Workload | 141 | ||