| Course Name | Code | Semester | T+U Hours | Credit | ECTS |
|---|---|---|---|---|---|
| Data Analysis With Phyton | ISE 448 | 8 | 3 + 0 | 3 | 5 |
| Precondition Courses | |
| Recommended Optional Courses | |
| Course Language | Turkish |
| Course Level | Bachelor's Degree |
| Course Type | Optional |
| Course Coordinator | Prof.Dr. İSMAİL HAKKI CEDİMOĞLU |
| Course Lecturers | |
| Course Assistants |
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| Course Category | Available Basic Education in the Field |
| Course Objective | Data Analysis with Python Programming Language, Basics of Python programming language is explained. The data analysis libraries of tPython programming language will be examined. Data analysis with Python programming language is carried out. |
| Course Content | Basic data types, Basic programming control structures, Data list creation and usage, tuples, Dictionaris, Arrays, Dataframe usage, Data analysis |
| # | Course Learning Outcomes | Teaching Methods | Assessment Methods |
|---|---|---|---|
| 1 | Apply decision and repetition structures in program design | Drilland Practice, Problem Solving, Project Based Learning, | Project / Design, |
| 2 | Implement functions to improve efficiency of programs | Lecture, Question-Answer, Discussion, Drilland Practice, | Project / Design, |
| 3 | Write Python program codes to analyse datasets | Lecture, Question-Answer, Discussion, Drilland Practice, Motivations to Show, | Testing, |
| 4 | Question-Answer, Discussion, Drilland Practice, Demonstration, Motivations to Show, | Testing, | |
| 5 | Question-Answer, Discussion, Drilland Practice, Demonstration, Motivations to Show, | Testing, |
| Week | Course Topics | Preliminary Preparation |
|---|---|---|
| 1 | Introduction to Python Programming | |
| 2 | Input, Processing and Output | |
| 3 | Decision Structures | |
| 4 | Looping Structures | |
| 5 | Functions | |
| 6 | File Handling | |
| 7 | Lists and Tuples | |
| 8 | Dictionaries | |
| 9 | Series | |
| 10 | DataFrames | |
| 11 | Python Libraries for Data Analysis | |
| 12 | Data Analysis Example | |
| 13 | Data Analysis Example | |
| 14 | Data Analysis Application |
| Resources | |
|---|---|
| Course Notes | <p>PPT Presentations, Python Program Codes.</p> |
| Course Resources | Python in 24 Hours, Sams Teach Yourself, Second Edition, Katie Cunningham, Sams Publihing, 2013. Machine Learning For Dummies, John Paul Mueller, Luca Massaron, John Wiley & Sons, 2016. Pyhton for Data Analysis, Wes McKinney, O'Reilly Media, Inc., 2018. |
| Order | Program Outcomes | Level of Contribution | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | X | ||||||
| 2 | X | ||||||
| 3 | X | ||||||
| 4 | X | ||||||
| 5 | X | ||||||
| 6 | X | ||||||
| 7 | X | ||||||
| 8 | |||||||
| 9 | X | ||||||
| 10 | X | ||||||
| 11 | X | ||||||
| 12 | X | ||||||
| Evaluation System | |
|---|---|
| Semester Studies | Contribution Rate |
| 1. Ödev | 100 |
| Total | 100 |
| 1. Yıl İçinin Başarıya | 40 |
| 1. Final | 60 |
| Total | 100 |
| ECTS - Workload Activity | Quantity | Time (Hours) | Total Workload (Hours) |
|---|---|---|---|
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 3 | 48 |
| Hours for off-the-classroom study (Pre-study, practice) | 12 | 3 | 36 |
| Mid-terms | 1 | 7 | 7 |
| Quiz | 1 | 7 | 7 |
| Assignment | 1 | 9 | 9 |
| Final examination | 1 | 12 | 12 |
| Total Workload | 119 | ||
| Total Workload / 25 (Hours) | 4.76 | ||
| dersAKTSKredisi | 5 | ||