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Information
| Code | ISB009 |
| Name | Data Analysis with Python |
| 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 | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
1 |
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 | ||