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
  • Faculty of Engineering
  • Dept.of Electrical and Electronics Engineering(Eng)
  • Course Structure Diagram with Credits
  • Engineering Mathematics and Statistics
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications

Course Introduction Information

Code - Course Title MAT293 - Engineering Mathematics and Statistics
Course Type Required Courses
Language of Instruction İngilizce
Laboratory + Practice 4+0
ECTS 7.0
Course Instructor(s) DOKTOR ÖĞRETİM ÜYESİ ALTAN ONAT
Mode of Delivery Face to face
Prerequisites EMAT111 Calculus I, EMAT112 Calculus II
Courses Recomended MAT 247 – Engineering Mathematics
Recommended Reading List Zill, D. G. (2014). Advanced engineering mathematics (5th ed.). Jones & Bartlett Learning.Zill, D., & Shanahan, P. (2003). A first course in complex analysis with applications. Jones & Bartlett Learning.Stewart, J. (2016). Calculus: Early transcendentals (8th ed.). Cengage Learning.Ross, S. (2021). Probability and statistics for engineers and scientists (6th ed.). Elsevier.
Assessment methods and criteria 2 midterm exams, 1 final exam
Work Placement Not Applicable
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Rotation of the coordinate axes, Vector calculus
Week - 2 Gradients, Divergence & curl of vector fields
Week - 3 Vector integration, Line & multiple integrals, Arc length, Surface area, Volume calculation
Week - 4 Cylindrical & spherical coordinates, Gauss’ theorem, Stokes' theorem
Week - 5 Complex algebra
Week - 6 1st Midterm Exam
Week - 7 Cauchy-Riemann conditions, Laplace equations, Exponential function
Week - 8 Complex integrations, Cauchy’s integral theorem
Week - 9 Singularities, Residue integration
Week - 10 Conformal mapping
Week - 11 Midterm 2
Week - 12 Data representation, Introduction to probability theory
Week - 13 Random sampling, Point estimation of parameters, Confidence intervals, Hypotheses testing (decisions)
Week - 14 Goodness of fit, Nonparametric tests, Regression, Fitting straight lines, Correlation

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Problem Solving
  • Competences
  • Questoning
  • Problem solving
  • Applying theoretical knowledge into practice
  • To work autonomously
  • Elementary computing skills

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 30
2.Midterm Exam 1 30
Final Exam 1 40
Toplam (%) 100
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