Akademik Dersler - Tanıtım


Code - Course Title	ENM421 - Introduction to Data Science
Course Type	Area Elective Courses
Language of Instruction	Türkçe
Laboratory + Practice	2+1
ECTS	4.5
Course Instructor(s)	DOKTOR ÖĞRETİM ÜYESİ ZELİHA ERGÜL AYDIN
Mode of Delivery	Face to face
Prerequisites	None.
Courses Recomended	Introduction to Computation and Program. for Industrial Eng.
Recommended Reading List	• Python Data Science Handbook (PDA), Jake VanderPlas, O’Reilly Media Inc., • J. Grus, “Data Science from Scratch: First Principles with Python” ,J. Stevenson, O’Reilly Media• Uygulamalarla Veri Bilimi, Deniz KILINÇ
Assessment methods and criteria	Midterms, Final, and Term Project
Work Placement	None.
Sustainability Development Goals

Weeks	Topics
Week - 1	Introduction: What is data science?
Week - 2	Data manipulation with Numpy and Pandas library.
Week - 3	Data visualization with Python Matplotlib and Seaborn libraries, and interpretation.
Week - 4	Data visualization with Python Matplotlib and Seaborn libraries, and interpretation.
Week - 5	Data analysis with Python statsmodels library.
Week - 6	Collect data from the different sources, clean, and analyze the data.
Week - 7	Regression models and Python application with scikit-learn library.
Week - 8	Classification models and Python application with scikit-learn library.
Week - 9	Clustering models and Python application with scikit-learn library.
Week - 10	Text processing applications.
Week - 11	Recommendation systems applications.
Week - 12	Image processing applications
Week - 13	Project presentations.
Week - 14	Project presentations.

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
1.Midterm Exam	1	20
Homework	1	10
Project	1	30
Final Exam	1	40
Toplam (%)		100