Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Institute of Graduate Programmes
  • Department of Statistics
  • Master of Science (MS) Degree
  • Program in Applied Statistics
  • Course Structure Diagram with Credits
  • R For Data Science
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load

Course Introduction Information

Code - Course Title İST552 - R For Data Science
Course Type Required Courses
Language of Instruction Türkçe
Laboratory + Practice 3+0
ECTS 7.5
Course Instructor(s) DOÇENT DOKTOR MUSTAFA ÇAVUŞ
Mode of Delivery FACE TO FACE
Prerequisites NONE
Courses Recomended NONE
Required or Recommended Resources Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. \" O\'Reilly Media, Inc.\".
Recommended Reading List Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. \" O\'Reilly Media, Inc.\".
Assessment methods and criteria MIDTERMHOMEWORKFINAL
Work Placement NONE
Sustainability Development Goals

Content

Weeks Topics
Week - 1 What is data science?
Week - 2 Visualization with ggplot
Week - 3 Visualization with ggplot
Week - 4 Data manipulation with dplyr
Week - 5 Data manipulation with dplyr
Week - 6 Explanatory data analysis
Week - 7 tidyverse package
Week - 8 tidyverse package
Week - 9 tidyverse package
Week - 10 lubridate package
Week - 11 magrittr package
Week - 12 modelr package
Week - 13 RMarkdown basics
Week - 14 RMarkdown formats

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Demonstration
  • Competences
  • Productive
  • Follow ethical and moral rules
  • Effective use of Turkish
  • Work in teams
  • Eleştirel düşünebilme
  • Abstract analysis and synthesis
  • Problem solving
  • Applying theoretical knowledge into practice
  • Information Management
  • To work autonomously
  • Organization and planning
  • Elementary computing skills
  • Decision making
  • Project Design and Management

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 20
2.Midterm Exam 1 30
Final Exam 1 50
Toplam (%) 100
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