• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Type English text

   Contribution to Program Outcomes

   1. Supporting complex problems in their fields with temporal and spatial data, and successfully solving them through statistical methods and numerical models
   2. To develop the knowledge of using different technical and modern tools and software for applications in the field

   Method of assessment

   1. Written exam
   2. Homework assignment
2. Type English text

   Contribution to Program Outcomes

   1. To become skillful to solve the problems encountered in the field
   2. Integrating the knowledge gained in the domain combined with information from different disciplines and creating new information
   3. Supporting complex problems in their fields with temporal and spatial data, and successfully solving them through statistical methods and numerical models
   4. To be able to construct a problem independently, develop a solution method, solve it, evaluate the results and apply when necessary

   Method of assessment

   1. Written exam
   2. Homework assignment
3. Type English text

   Contribution to Program Outcomes

   1. To become skillful to solve the problems encountered in the field
   2. Supporting complex problems in their fields with temporal and spatial data, and successfully solving them through statistical methods and numerical models
   3. To develop the knowledge of using different technical and modern tools and software for applications in the field
   4. To adopt these values ​​by considering the social, scientific, cultural and ethical values ​​in the stages of data collection, interpretation, application and declaration by doing field studies related to the field

   Method of assessment

   1. Written exam
   2. Homework assignment

Contents
Week 1:	Introduction scientific programming
Week 2:	Python and other programming languages
Week 3:	Variables and data types, Homework 1
Week 4:	Operators and regular expressions, Homework 2
Week 5:	Selection control structures, Homework 3
Week 6:	Methods and Functions, , Homework 4
Week 7:	Midterm exam, File operations,
Week 8:	Object Oriented Programming, Homework 5
Week 9:	Matrices and Algebric Equations, Homework 6
Week 10:	Working with dataframes, Homework 7
Week 11:	Algorithms for scientific computing, Homework 8
Week 12:	Exploratory data analysis, Homework 9
Week 13:	Libraries for visualization, Homework 10
Week 14:	Working with geospatial data
Week 15*:	-
Week 16*:	Final Exam
Textbooks and materials:	C. Hill, (2010), Learning Scientific Programming with Python, Cambridge University Press. C. Führer, J. E. Solem, O. Verdier, (2021), Scientific Computing with Python: High-performance scientific computing with NumPy, SciPy, and pandas, Packt Publishing.
Recommended readings:	Chapra S.C., Clough D. Applied Numerical Methods with Python for Engineers and Scientists. McGraw Hill. Sundnes J. Introduction to Scientific Programming with Python, Springer.
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	7	30
Other in-term studies:		0
Project:		0
Homework:	3,4,5,6,7,9,10,11,12,13	30
Quiz:		0
Final exam:	16	40
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	3	14
Practice, Recitation:	0	0
Homework:	2.5	10
Term project:	0	0
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	6	6
Mid-term:	2	1
Personal studies for final exam:	6	6
Final exam:	3	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)