BL142 Advanced Python

4 ECTS - 3-0 Duration (T+A)- 2. Semester- 3 National Credit

Information

Code BL142
Name Advanced Python
Semester 2. Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 4 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Belirsiz
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Mahir ATMIŞ


Course Goal

The main purpose of this course is to teach students the concepts of object-oriented programs to the students who has the basic knowledge of python programming and to provide information about how to use scientific libraries.

Course Content

This course aims to teach the concepts such as class, object, inheritance, multi -formality, the use of NumPy, and Pandas libraries, data visualization and data analysis

Course Precondition

None

Resources

Ders Notları Mahir Atmış

Notes

Veri Bilimi İçin Python Eğitim Kitabı, Dr. Bülent ÇOBANOĞLU


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Knows the concept of class.
LO02 Learns the logic of object-oriented programming.
LO03 Learns the use of ready libraries.
LO04 Uses high -performance libraries in solving complex scientific problems.
LO05 Learns to draw statistics through data.
LO06 Learns to perform visualization using data.
LO07 Learns to clean the data.
LO08 Gains the ability to obtain information from raw data.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Explain the basic scientific concepts related to Computer Technologies.
PLO02 Beceriler - Bilişsel, Uygulamalı Can use algorithmic thinking & planning approaches in programming.
PLO03 Beceriler - Bilişsel, Uygulamalı uses word processor, spreadsheet, presentation programs.
PLO04 Bilgi - Kuramsal, Olgusal Has the ability to solve problems in the field of computer programming. 4
PLO05 Bilgi - Kuramsal, Olgusal Knows the basic electronic parts of computer hardware and their functioning.
PLO06 Beceriler - Bilişsel, Uygulamalı Basic level Database Systems, client/server software and implements
PLO07 Beceriler - Bilişsel, Uygulamalı In Computer Technologies, students use graphical programs used in interface design and 3D modeling in web pages at basic level.
PLO08 Beceriler - Bilişsel, Uygulamalı Explains, designs and installs network systems.
PLO09 Yetkinlikler - Alana Özgü Yetkinlik Uses Internet technologies, develops server-side working internet applications.
PLO10 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Can carry out a basic study related to the field independently or in disciplined teams
PLO11 Yetkinlikler - Öğrenme Yetkinliği Can do resource research and obtain information from database in order to follow the developments in the field with the necessity of lifelong learning.
PLO12 Bilgi - Kuramsal, Olgusal Knows a foreign language which is sufficient for the applications in the field.
PLO13 Bilgi - Kuramsal, Olgusal To be able to communicate effectively in written and oral Turkish.
PLO14 Yetkinlikler - İletişim ve Sosyal Yetkinlik He/she can clearly explain the designs and applications related to computer technologies to his colleagues, superiors, others who are related to the field or not. 3
PLO15 Bilgi - Kuramsal, Olgusal Has knowledge about Atatürk's Principles and History of Revolution.
PLO16 Yetkinlikler - İletişim ve Sosyal Yetkinlik It is aware of occupational health and safety, environmental and ethical values within the framework of global and social values.


Week Plan

Week Topic Preparation Methods
1 Classes and methods Preparation is not required. Öğretim Yöntemleri:
Anlatım
2 Classes and methods (cont.) Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
3 Inheritance Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
4 Polymorphism Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
5 Abstraction Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
6 Encapsulation Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
7 Exception Handling Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
8 Mid-Term Exam Ölçme Yöntemleri:
Yazılı Sınav
9 Numpy Arrays Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
10 Numpy Slicing Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
11 Numpy Random Numbers Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
12 Pandas Series and Dataframes Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
13 Pandas File Manipulation Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
14 Pandas Data Preprocessing Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
15 Pandas Data Visualization Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
16 Term Exams Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams 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 3 42
Assesment Related Works
Homeworks, Projects, Others 1 3 3
Mid-term Exams (Written, Oral, etc.) 1 5 5
Final Exam 1 18 18
Total Workload (Hour) 110
Total Workload / 25 (h) 4,40
ECTS 4 ECTS