|
|
| HRT2051 | Professional Computer Programming | 2+1+0 | ECTS:3 | | Year / Semester | Fall Semester | | Level of Course | First Cycle | | Status | Compulsory | | Department | DEPARTMENT of GEOMATICS ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 2 hours of lectures and 1 hour of practicals per week | | Lecturer | Doç. Dr. Mustafa DİHKAN | | Co-Lecturer |
| | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | To enable students to develop programs in Python language related to important topics in cartography. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | You will learn data structures in Python | 4.1 | 4, | | LO - 2 : | Learn Python loops | 4.2 | 4, | | LO - 3 : | Able to do Data Analysis with Python | 4.1 | 4, | | LO - 4 : |
Solve Engineering Problems with Python | 4.2 | 4, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| Data structures in Python, Conditions in Python, Loops in Python, File Operations in Python, Data Analysis with Python |
| |
| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Data structures in Python: List, Dict, Tuple and Boolean
| | | Week 2 | Conditions in Python | | | Week 3 | Loops in Python | | | Week 4 | Methods and Functions in Python | | | Week 5 |
File Operations, Modules, and Packages in Python | | | Week 6 | Commonly used libraries in Python: NumPy Features, Conditional Element Operations, Mathematical Operations with NumPy, Equation Solutions with NumPy | | | Week 7 | Commonly used libraries in Python: Pandas Properties, Creating Pandas Series, Conditional Element Operations, Pivot Tables | | | Week 8 | Data Analysis with Python: Data Preprocessing, Missing Data Analysis, Outlier Data Analysis, Data Visualization, Normalization of Variables, Categorical Data Processing, Variable Transformations, Time Series Data Analysis | | | Week 9 | Midterm exam
| | | Week 10 | Data Visualization with Python | | | Week 11 | Professional practice 1-2 | | | Week 12 | Professional practice 3-4
| | | Week 13 | Professional practice 5-6 | | | Week 14 | Professional practice 7-8 | | | Week 15 | Professional practice 9-10 | | | Week 16 | Final exam | | | |
| 1 | McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. " O'Reilly Media, Inc.". | | | |
| 1 | VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data. " O'Reilly Media, Inc.". | | | 2 | Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O'Reilly Media, Inc.". | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 1 | 30 | | Laboratory exam | 12 | | 1 | 20 | | End-of-term exam | 16 | | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 4 | 14 | 56 | | Sınıf dışı çalışma | 6 | 10 | 60 | | Laboratuar çalışması | 2 | 5 | 10 | | Arasınav için hazırlık | 3 | 1 | 3 | | Arasınav | 2 | 1 | 2 | | Dönem sonu sınavı için hazırlık | 4 | 1 | 4 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 137 |
|