Akademik Dersler - Tanıtım


Code - Course Title	İST559 - Data Science with Julia
Course Type	Elective Courses
Language of Instruction	Türkçe
Laboratory + Practice	3+0
ECTS	7.5
Course Instructor(s)	DOKTOR ÖĞRETİM ÜYESİ İSMAİL YENİLMEZ
Mode of Delivery	The mode of delivery of this course is face to face.
Prerequisites	There is no recommended optional programme component for this course.
Courses Recomended	There is no recommended optional programme component for this course.
Recommended Reading List	https://julialang.org/learning/mooc/
Assessment methods and criteria	1 Midterm Exam, 1 Homework and Final Exam (Classic)
Work Placement	Not suitable for this course.
Sustainability Development Goals

Weeks	Topics
Week - 1	Fundamentals for Data Science
Week - 2	Fundamentals for Data Science
Week - 3	Frequently Used Programming Languages for Data Science
Week - 4	Introduction to the Julia Programming Language: Installation, environment, and tools
Week - 5	Basic programming with Julia
Week - 6	Conditional statements with Julia
Week - 7	Loops and functions with Julia
Week - 8	Importing data into the programming environment with Julia
Week - 9	Data manipulation with Julia
Week - 10	Machine learning with Julia: Supervised learning
Week - 11	Machine learning with Julia: Unsupervised learning
Week - 12	Advantages of Julia in computational science
Week - 13	From Julia to other programming languages
Week - 14	Reproducibility

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
Homework	1	40
Final Exam	1	60
Toplam (%)		100