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Information
| Code | YZ005 |
| Name | Data analysis and data visualization with Python programming language |
| Term | 2024-2025 Academic Year |
| Term | Fall |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
1 |
Course Goal / Objective
To teach the basic concepts and methods of data analysis and to perform statistical and visual analysis and interpretation of data with the help of Pyhton programming language.
Course Content
To teach the basic concepts and methods of data analysis and to perform statistical and visual analysis and interpretation of data with the help of Pyhton programming language.
Course Precondition
There is no prerequisite for the course.
Resources
İlker Arslan, PYTHON ile Veri Bilimi, Pusula Yayıncılık Ve İletişim, 2021, 978-605-2359-64-8
Notes
Volkan Taşçı, Python Eğitim Kitabı, Dikeyeksen Yayıncılık, 2021, 978-605-4898-70-1
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Learn the basics of Pyhton programming language. |
| LO02 | Evaluate data using the knowledge and skills acquired in mathematics or computer science. |
| LO03 | Use popular Python libraries for data analysis and visualization effectively. |
| LO04 | Interpret data using the knowledge and skills acquired in mathematics or computer science. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Beceriler - Bilişsel, Uygulamalı | To be able to access information broadly and deeply by conducting scientific research in the field, to be able to evaluate, interpret and apply the information. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Has a comprehensive knowledge of current techniques and methods applied in engineering and their limitations. | 4 |
| PLO03 | Beceriler - Bilişsel, Uygulamalı | To be able to use uncertain, limited or incomplete data to complete and apply knowledge using scientific methods; to be able to use knowledge from different disciplines together. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Is aware of new and emerging practices of the profession, examines and learns them when needed. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Defines and formulates problems related to the field, develops methods to solve them and applies innovative methods in solutions. | 4 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Develops new and/or original ideas and methods; designs complex systems or processes and develops innovative/alternative solutions in their designs. | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Designs and implements theoretical, experimental and modeling-based research; examines and solves complex problems encountered in this process. | |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | To be able to work effectively in disciplinary and multidisciplinary teams, to lead such teams and to develop solution approaches in complex situations; to be able to work independently and take responsibility. | 4 |
| PLO09 | Bilgi - Kuramsal, Olgusal | To be able to communicate orally and in writing in a foreign language at least at the B2 level of the European Language Portfolio. | |
| PLO10 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | To be able to communicate the process and results of his/her studies systematically and clearly in written or oral form in national and international environments in or outside the field. | |
| PLO11 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Knows the social, environmental, health, safety, legal, project management and business life practices of engineering applications and is aware of the constraints these impose on engineering applications. | |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in the stages of data collection, interpretation and announcement and in all professional activities. | 4 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Data Science | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 2 | Descriptive statistics; Frequency Distribution, Measures of Central Tendency and Variability | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 3 | Statistical Estimation and Hypothesis Testing | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 4 | Python Basics | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 5 | Python Basics, continued | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 6 | Numpy Library | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 7 | Pandas Library, Matplotlib Library | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Data preparation, cleaning and processing | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 10 | Seaborn Library | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 11 | Data visualization with Seaborn | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 12 | Ploty Library | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 13 | Rarely Used Visualization Tools | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 14 | Sample Project Application 1 | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 15 | Sample project Application 2 | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 15 | 15 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 20 | 20 |
| Total Workload (Hour) | 162 | ||
| Total Workload / 25 (h) | 6,48 | ||
| ECTS | 6 ECTS | ||