| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| SEN4018 | Data Science with Python | Fall | 3 | 0 | 3 | 6 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi BETÜL ERDOĞDU ŞAKAR |
| Course Objectives: | The aim of this course is teaching students to extract knowledge from data. Students will learn concepts, techniques and tools they need to deal with various facets of data science practice, including data analysis, predictive modeling, descriptive modeling and evaluation. The focus in the treatment of these topics will be on breadth, rather than depth, and emphasis will be placed on integration and synthesis of concepts and their application to solving problems. To make the learning contextual, real datasets from a variety of disciplines will be used with Python. |
|
The students who have succeeded in this course; 1) Understand and use data exploration techniques. 2) Understand and apply data pre-processing, transformation, normalization and standardization. 3) Interpret data and visualize it accordingly. 4) Interpret and use different supervised and unsupervised learning algorithms 5) Use and understand different supervised and unsupervised learning evaluation method. 6) Demonstrates the ability to use libraries prepared for Python when developing applications. 7) Conduct independent, limited data collection, analysis, and evaluation according to established engineering principles in accordance with current research ethical standards. |
| Introduction and Programming Review with Python Arrays, Matrices, Mathematical Functions with Numpy Data Manipulation and Analysis with Pandas Data Pre-Processing, Transformation, Normalization, Standardization Data Visualization Supervised Learning - Regression Supervised Learning - Classification Cross Validation Evaluation Metrics Unsupervised Learning |
| Week | Subject | Related Preparation | |
| 1) | Introduction and Programming Review with Python | ||
| 2) | Arrays, Matrices, Mathematical Functions with Numpy | ||
| 3) | Data Manipulation and Analysis with Pandas | ||
| 4) | Data Pre-Processing, Transformation, Normalization, Standardization | ||
| 5) | Data Visualization | ||
| 6) | Supervised Learning - Regression | ||
| 7) | Supervised Learning - Regression | ||
| 8) | Supervised Learning - Regression | ||
| 9) | Supervised Learning - Classification | ||
| 10) | Supervised Learning - Classification | ||
| 11) | Supervised Learning - Classification | ||
| 12) | Cross Validation | ||
| 13) | Evaluation Metrics | ||
| 14) | Unsupervised Learning | ||
| Course Notes: | Ethem Alpaydın, Introduction To Machine Learning, 3rd Edition, MIT Press, 2015, ISBN-13: 978-8120350786. |
| References: | Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Introduction to Data Mining, 1st Edition, Pearson, 2005, ISBN-13: 978-0321321367. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006, ISBN-13: 978-0387310732. |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | 6 | % 25 |
| Homework Assignments | % 0 | |
| Presentation | % 0 | |
| Project | 1 | % 20 |
| Seminar | % 0 | |
| Midterms | 1 | % 15 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Special Course Internship (Work Placement) | 0 | 0 | 0 |
| Field Work | 0 | 0 | 0 |
| Study Hours Out of Class | 14 | 2 | 28 |
| Presentations / Seminar | 0 | 0 | 0 |
| Project | 6 | 3 | 18 |
| Homework Assignments | 0 | 0 | 0 |
| Quizzes | 6 | 3 | 18 |
| Preliminary Jury | 0 | 0 | 0 |
| Midterms | 6 | 3 | 18 |
| Paper Submission | 0 | 0 | 0 |
| Jury | 0 | 0 | 0 |
| Final | 6 | 3 | 18 |
| Total Workload | 142 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |