Erciyes University - Info Package

Dersin Adı	Dersin Seviyesi	Dersin Kodu	Dersin Tipi	Dersin Dönemi	Yerel Kredi	AKTS Kredisi	Ders Bilgileri
INTRODUCTION TO DATA SCIENCE	Birinci Düzey	YZ 311	Zorunlu	5	5.00	5.00	Yazdır

Dersin Tanımı

Ön Koşul Dersleri
Eğitimin Dili	English
Koordinatör	DR. ÖĞR. ÜYESİ RUKİYE NUR KAÇMAZ
Dersi Veren Öğretim Eleman(lar)ı
Yardımcı Öğretim Eleman(lar)ı
Dersin Veriliş Şekli
Dersin Amacı	The primary objective of this course is to provide students with a solid foundation in the mathematical and statistical methods required for data-driven decision-making. It aims to develop engineering competencies in importing, visualizing, and analyzing large datasets using modern informatics tools.
Dersin Tanımı	This course offers a comprehensive introduction to the data science lifecycle, beginning with data preprocessing, visualization, and the application of statistical learning principles. Students gain hands-on experience in addressing complex engineering problems by evaluating model performance and implementing ensemble techniques.

Dersin İçeriği

1	Introduction To Data Science.
2	Importing, Summarizing and Visualizing Data.
3	Statistical Learning.
4	Monte Carlo Methods.
5	Monte Carlo for Optimization.
6	Unsupervised Learning.
7	Regression.
8	Midterm Exam.
9	Regularization and Kernel Methods.
10	Classification.
11	Decision Trees
12	Ensemble Methods.
13	Project Presentation.
14	Project Presentation.
15	Final Exam.
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Dersin Öğrenme Çıktıları

1	Utilizes modern informatics tools and programming environments to import, summarize, and visualize data.
2	Applies engineering knowledge of mathematics and statistical learning to formulate solutions for data science problems.
3	Identifies and formulates complex engineering problems using predictive modeling techniques such as regression and classification.
4	Designs and optimizes machine learning architectures, including Decision Trees and Ensemble Methods, to solve specific engineering challenges.
5	Analyzes and evaluates model performance through experimental methods like Monte Carlo simulations.
6	Works effectively within a team to manage a data science project and communicates technical findings through oral presentations.
7	Demonstrates the ability to adapt to emerging technologies in regularization and kernel methods through independent research.
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*Dersin Program Yeterliliklerine Katkı Seviyesi

1	Applies engineering knowledge related to mathematics, science, basic engineering, and computer-based calculations to solve engineering problems.
2	Gains the ability to develop engineering solutions using discipline-specific knowledge and methods.
3	Defines complex engineering problems and formulates them using fundamental science and engineering knowledge.
4	Defines and analyzes solutions to problems, taking into account the UN Sustainable Development Goals.
5	Designs creative solutions to complex engineering problems.
6	Gains the ability to design complex systems, processes, devices, or products by considering realistic constraints in the engineering problem-solving process.
7	For the analysis and solution of complex engineering problems, selects and effectively applies appropriate methods, techniques, resources, and modern engineering and computing tools, including estimation and modeling.
8	Aware of the limitations of the methods, techniques, and IT tools used, it produces and implements appropriate solutions.
9	Conducts literature research to examine complex engineering problems and collects data within the scope of these studies.
10	Using research methods, the student designs and conducts experimental or applied studies, analyze and evaluate the results.
11	Within the scope of the UN Sustainable Development Goals, the student is knowledgeable about the impacts of engineering solutions on society, health and safety, the economy, sustainability, and the environment, and analyze these impacts.
12	Gains awareness of the legal consequences of engineering solutions.
13	The student acts in accordance with professional principles and legal regulations in engineering practice and acquires knowledge about ethical responsibilities.
14	Gains awareness about non-discrimination, impartiality, and inclusiveness of diversity.
15	Gains the ability to work effectively as an individual, team member, or team leader in intra-disciplinary or multi-disciplinary team projects.
16	Gains the ability to communicate effectively in technical matters, both verbally and in writing by taking into account the differences in the target audience''s education, language, and profession.
17	Gains the ability to apply project management principles, perform time and resource planning, and conduct economic feasibility analysis.
18	Gains awareness about entrepreneurship and innovation.
19	Gains the ability to adapt to new and emerging technologies and to evaluate technological changes with a questioning and critical perspective.
20	Gains the ability to independently and continuously learn new knowledge and skills.
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Yıldızların sayısı 1’den (en az) 5’e (en fazla) kadar katkı seviyesini ifade eder

Planlanan Öğretim Faaliyetleri, Öğretme Metodları ve AKTS İş Yükü

	Sayısı	Süresi (saat)	Sayı*Süre (saat)
Yüz yüze eğitim	0	0	0
Sınıf dışı ders çalışma süresi (ön çalışma, pekiştirme)	0	0	0
Ödevler	0	0	0
Sunum / Seminer hazırlama	0	0	0
Kısa sınavlar	0	0	0
Ara sınavlara hazırlık	0	0	0
Ara sınavlar	0	0	0
Proje (Yarıyıl ödevi)	0	0	0
Laboratuvar	0	0	0
Arazi çalışması	0	0	0
Yarıyıl sonu sınavına hazırlık	0	0	0
Yarıyıl sonu sınavı	0	0	0
Araştırma	0	0	0
Toplam iş yükü			0
AKTS			0.00

Değerlendirme yöntemleri ve kriterler

Yarıyıl içi değerlendirme	Sayısı	Katkı Yüzdesi
Ara sınav	1	100
Kısa sınav	0	0
Ödev	0	0
Yarıyıl içi toplam		100
Yarıyıl içi değerlendirmelerin başarıya katkı oranı		40
Yarıyıl sonu sınavının başarıya katkı oranı		60
Genel toplam		100

Önerilen Veya Zorunlu Okuma Materyalleri

Ders kitabı	Data Science and Machine Learning Mathematical and Statistical Methods; Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre, Radislav Vaisman
Yardımcı Kaynaklar

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