• 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 VYA 5017 STATISTICAL PROGRAMMING LANGUAGES COMPULSORY 3 0 0 7

Offered By

DATA MANAGEMENT AND ANALYSIS

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR IPEK DEVECI KOCAKOÇ

Offered to

DATA MANAGEMENT AND ANALYSIS

Course Objective

The aim of this course is to create the necessary programming infrastructure for data analysis. After giving the basic algorithm and flow order logic, the coding will be done with the current programming languages (R, Python, etc.).

Learning Outcomes of the Course Unit

1   1. To able to identify the program suitable for the problem 2   2. To able to design the implemantation phase of a programming project 3   3. To able to use abstraction methods. 4   4. To able to apply software development method in problem solving. 5   5. To able to evaluate critical methods ,tools and technologies used in software projects

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Programming Languages and basic concepts 2 Basic Programming: Variables, data structures and operations with data 3 Basic programming: selection ,loops and methods. 4 Basic programming: one- dimentional and multi-dimentional arrays 5 Introduction to R Language 6 Basic data types, commands and interfaces 7 Use of packages 8 Working with data sets 9 Data extraction 10 Data visualization 11 Basic statistics 12 R Markdown 13 Programming examples 14 Programming examples

Recomended or Required Reading

R for Data Science, Garrett Grolemund, Hadley Wickham

Planned Learning Activities and Teaching Methods

1- Lecture Method, 2- Demonstration Method with Applications, 3-Discussions

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

Further Notes About Assessment Methods

None

Assessment Criteria

Students will be evaluated according to their written and oral presentations by carrying out a project during the semester. In the final exam at the end of the semester, another probing experience will be evaluated according to the application skills

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 14 3 42 Preparations before/after weekly lectures 13 3 39 Preparation for midterm exam 1 20 20 Preparation for final exam 1 30 30 Preparing assignments 5 10 50 Final 1 3 3 Midterm 1 3 3 TOTAL WORKLOAD (hours) 187

Contribution of Learning Outcomes to Programme Outcomes

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