Akademik Dersler - Tanıtım


Code - Course Title	YBL519 - Introduction to Data Science with Python for Earth Sciences
Course Type	Elective Courses
Language of Instruction	Türkçe
Laboratory + Practice	3+0
ECTS	7.5
Course Instructor(s)	PROFESÖR DOKTOR HAKAN AHMET NEFESLİOĞLU
Mode of Delivery	Face-to-face/Online
Prerequisites	None
Courses Recomended	None
Required or Recommended Resources	Langtangen, H.P., 2016. A Primer on Scientific Programming with Python. Springer, Heidelberg, 942 p.PetrelliI, M., 2021. Introduction to Python in Earth Science Data Analysis from Descriptive Statistics to Machine Learning. Springer, Heidelberg, 252 p. VanderPlas, J., 2017. Python Data Science Handbook Essential Tools for Working with Data. O’Reilly Media, Inc., Sebastopol, 530 p.
Recommended Reading List	Ayer, V.M., Miguez, S., Toby, B.H., 2014. Why scientists should learn to program in Python. Powder Diffraction 29 (S2), 48-64. Kirk, M., 2017. Thoughtful Machine Learning with Python. O’Reilly Media, Inc., Boston, 204 p.Lin, J.W., 2012. Why Python Is the Next Wave in Earth Sciences Computing. Bulletin of the American Meteorological Society, 93, 12, 1823-1824.
Assessment methods and criteria	1 Midterm ( ), 3 Homework (0), 1 Final (P)
Work Placement	None
Sustainability Development Goals

Weeks	Topics
Week - 1	Introduction; Python Programming Fundamentals; Loops and Lists
Week - 2	Python Programming Fundamentals; Functions and Conditional Statements
Week - 3	Python Programming Fundamentals; File Operations and Error Handling
Week - 4	Python Programming Fundamentals; Dictionaries and Stings
Week - 5	Python Programming Fundamentals; Classes and Objects
Week - 6	Introduction to NumPy Library
Week - 7	Data Manipulation: Pandas
Week - 8	Data Visualization: Matplotlib
Week - 9	Introduction to Machine Learning
Week - 10	Naive Bayes Classification and Linear Regression
Week - 11	Support Vector Machines and Decision Trees
Week - 12	Principal Component Analysis and Manifold Learning
Week - 13	K-Means Clustering and Gaussian Mixture Models
Week - 14	Kernel Density Estimation

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
1.Midterm Exam	1	20
2.Midterm Exam	1	20
Final Exam	1	60
Toplam (%)		100