• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EKO 5083 ECONOMETRIC THEORY I COMPULSORY 3 0 0 4

Offered By

Econometrics

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR ŞENAY ÜÇDOĞRUK BIRECIKLI

Offered to

Econometrics

Course Objective

The main objective of the course is to define methods for linear programming and mathemmatical modelling as decision-support aid in companies.

Learning Outcomes of the Course Unit

1   To be able to define basic principles of optimisation. 2   To be able to connect to databases using R and MATLAB. 3   To be able to write code for data analysis using R and MATLAB. 4   To be able to use loops and functions in codes. 5   To be able to visualize data using R and MATLAB.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction to R, R Studio and Matlab 2 Basics of programming 3 Loops and counters 4 Data manipulation in R 5 Matrix and data structures in R 6 Data visualization in R 7 Data analysis and optimization in R 8 Mid-term 9 Data manipulation in Matlab 10 Matrix and data structures in Matlab 11 Data visualization in Matlab 12 Toolboxes 13 Data analysis and optimization in Matlab 14 Article discussions

Recomended or Required Reading

Satman, M. H. (2018) R ile Programlama, Türkmen Kitabevi, Istanbul. Deveci Kocakoç, I. (2007) MATLAB ve Istatistiksel Veri Analizi, Nobel, Ankara

Planned Learning Activities and Teaching Methods

This course will be presented using class lectures, class discussions, overhead projections, and demonstrations.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 STT TERM WORK (SEMESTER) 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.30 + FIN* 0.40 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.30 + RST* 0.40 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparations before/after weekly lectures 10 2 20 Preparation for midterm exam 1 10 10 Preparation for final exam 1 10 10 Midterm 1 3 3 Final 1 3 3 TOTAL WORKLOAD (hours) 85

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 LO.1 1 1 1 1 LO.2 1 1 1 1 LO.3 1 1 1 1 LO.4 1 1 1 1 LO.5 1 1 1 1