ISB009 Data Analysis with Python

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

Information

Code ISB009
Name Data Analysis with Python
Term 2024-2025 Academic Year
Semester . Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Doktora Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator


Course Goal / Objective

To provide students with the ability to apply the theoretical knowledge within the framework of data science in the Python program.

Course Content

Pyhton Language Basics, Built-in Data Structures, Functions, and Files, NumPy Basics, Pandas Library, Data Loading, Strorage, and File Formats, Data Cleaning and Preparation, Data Wrangling, Plotting and Visualization, Data Aggregation and Group Operations, Data Analysis Examples.

Course Precondition

none

Resources

McKinney, W. 2018. Python for Data Analysis. OReilly Media, 2nd edition

Notes

lecture notes


Course Learning Outcomes

Order Course Learning Outcomes
LO01 To be able to explain the basic concepts of the Python program
LO02 To be able to loop in the Python program
LO03 To be able to make basic statistical analyzes with the Python program
LO04 To be able to draw graphics with the Python program
LO05 To use libraries in Python program


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Develops new methods and strategies in modeling statistical problems and generating problem-specific solutions. 3
PLO02 Bilgi - Kuramsal, Olgusal Can do detailed research on a specific subject in the field of statistics. 4
PLO03 Bilgi - Kuramsal, Olgusal Have a good command of statistical theory to contribute to the statistical literature. 4
PLO04 Bilgi - Kuramsal, Olgusal Can use the knowledge gained in the field of statistics in interdisciplinary studies. 4
PLO05 Yetkinlikler - Öğrenme Yetkinliği Can organize projects and events in the field of statistics. 5
PLO06 Yetkinlikler - Öğrenme Yetkinliği Can perform the stages of creating a project, executing it and reporting the results. 5
PLO07 Beceriler - Bilişsel, Uygulamalı Have the ability of scientific analysis. 3
PLO08 Bilgi - Kuramsal, Olgusal Can produce scientific publications in the field of statistics.
PLO09 Bilgi - Kuramsal, Olgusal Have analytical thinking skills. 4
PLO10 Yetkinlikler - Öğrenme Yetkinliği Can follow professional innovations and developments both at national and international level. 4
PLO11 Yetkinlikler - Öğrenme Yetkinliği Can follow statistical literature. 4
PLO12 Beceriler - Bilişsel, Uygulamalı Can improve his/her foreign language knowledge at the level of making publications and presentations in a foreign language. 5
PLO13 Bilgi - Kuramsal, Olgusal Can use information technologies at an advanced level. 5
PLO14 Bilgi - Kuramsal, Olgusal Have the ability to work individually and make independent decisions. 5
PLO15 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Have the qualities necessary for teamwork. 5
PLO16 Bilgi - Kuramsal, Olgusal Have a sense of professional and ethical responsibility. 3
PLO17 Bilgi - Kuramsal, Olgusal Acts in accordance with scientific ethical rules. 3


Week Plan

Week Topic Preparation Methods
1 Python language basics Reading the related references Öğretim Yöntemleri:
Alıştırma ve Uygulama
2 Built-in data structures, functions, files Reading the related references Öğretim Yöntemleri:
Alıştırma ve Uygulama
3 Numpy basics Reading the related references Öğretim Yöntemleri:
Alıştırma ve Uygulama
4 Getting started with pandas Reading the related references Öğretim Yöntemleri:
Alıştırma ve Uygulama
5 Data loading, storage, and files Reading the related references Öğretim Yöntemleri:
Alıştırma ve Uygulama
6 Data cleaning and preparation Reading the related references Öğretim Yöntemleri:
Alıştırma ve Uygulama
7 Data combining, reshaping Reading the related references Öğretim Yöntemleri:
Alıştırma ve Uygulama
8 Project preparation Reading the related references Ölçme Yöntemleri:
Performans Değerlendirmesi
9 Plotting and visualization Reading the related references Öğretim Yöntemleri:
Alıştırma ve Uygulama
10 Data aggregation and group operations Reading the related references Öğretim Yöntemleri:
Alıştırma ve Uygulama
11 Time series with Python Reading the related references Öğretim Yöntemleri:
Alıştırma ve Uygulama
12 Advanced pandas Reading the related references Öğretim Yöntemleri:
Alıştırma ve Uygulama
13 Modeling libraries in Python Reading the related references Öğretim Yöntemleri:
Alıştırma ve Uygulama
14 Data Analysis Data compilation Öğretim Yöntemleri:
Gösterip Yaptırma, Alıştırma ve Uygulama
15 Data analysis on high dimensional data Data compilation Öğretim Yöntemleri:
Alıştırma ve Uygulama, Gösterip Yaptırma
16 Project presentation Data compilation, reporting Öğretim Yöntemleri:
Alıştırma ve Uygulama, Gösterip Yaptırma
17 Final Examination Reading the related references Ölçme Yöntemleri:
Performans Değerlendirmesi


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 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 15 15
Final Exam 1 30 30
Total Workload (Hour) 157
Total Workload / 25 (h) 6,28
ECTS 6 ECTS

Update Time: 09.05.2024 02:16