Computer Engineering (Thesis)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

General Introduction Information of the Course

Course Code:

UVB566

Course Title:

Applied Data Science with Machine Learning

Course Semester:

Fall

Ders Kredileri:

Theoretical	Practical	Credit	ECTS
3	0	3	8

Language of instruction:

Course Prerequisites:

Does the Course Require Work Experience?:

Type of course:

Departmental Elective

Course Level:

Master

TR-NQF-HE:7. Master`s Degree

QF-EHEA:Second Cycle

EQF-LLL:7. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Dr.Öğr.Üyesi Bilge SERDARER KUZU

Course Lecturer(s):

Course Assistants:

Course Objectives and Content

Course Objectives:

The aim of this course is to enable students to execute an end-to-end applied data science workflow with machine learning (problem framing → data preparation → feature engineering → modeling → validation/evaluation → interpretation → reporting) on real-world datasets. The course emphasizes experimental design (train/validation/test), cross-validation, hyperparameter tuning, error analysis, and reproducible pipelines, while also introducing model documentation (model cards) and sustainability/technical-debt awareness in ML systems.

Course Content:

Applied data science workflow; data wrangling/cleaning/EDA; splitting strategies and leakage awareness; feature engineering and transformations; core supervised learning models for regression and classification; evaluation metrics and cross-validation; reproducible experimentation with scikit-learn Pipelines; hyperparameter optimization; model comparison and error analysis; basic interpretability practices; model documentation (model cards); technical debt and maintainability risks in ML systems; hands-on assignments and a term project.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Explains the core steps and concepts of the applied data science and machine learning pipeline.
2) Explains the relationship between generalization, overfitting/underfitting, cross-validation, and metric selection.
2 - Skills
Cognitive - Practical
1) Cleans, transforms, and performs feature engineering to create a modeling-ready dataset.
2) Builds scikit-learn pipelines; compares and selects models using cross-validation and hyperparameter search.
3 - Competences
Communication and Social Competence
1) Reports and presents results (metrics, error analysis, limitations) appropriately for the target audience.
2) Coordinates data preparation, modeling, and evaluation tasks to deliver team outputs.
Learning Competence
1) Learns a new method/tool from documentation (e.g., a new scikit-learn component) and integrates it into the project.
2) Iteratively improves (features, model, parameters, split strategy) and justifies decisions based on experimental results.
Field Specific Competence
1) Translates the problem into measurable success criteria; defines baselines and a validation design.
2) Documents the model for transparency and responsible use (model cards) and states intended use boundaries.
Competence to Work Independently and Take Responsibility
1) Plans and delivers an end-to-end ML project reproducibly (code, parameters, experiment records).
2) Evaluates maintenance/technical debt risks in ML systems and proposes mitigation practices for sustainability.

Course Weekly Plan

Week	Subject	Related Preparation
1)	Introduction; DS/ML pipeline; problem framing & success criteria
2)	Veri anlama (EDA), veri kalitesi; train/validation/test bölme mantığı
3)	Preprocessing: missing/outliers, scaling/encoding; leakage awareness
4)	Baselines; regression fundamentals; error metrics
5)	Classification fundamentals; metrics (precision/recall/F1, ROC-AUC)
6)	Cross-validation; model selection; proper comparison
7)	End-to-end experiments with pipelines; hyperparameter search
8)	Midterm Exam
9)	Deeper feature engineering; categorical/numeric transforms; (opt.) feature selection
10)	Intro to ensembles (bagging/boosting intuition) and error analysis
11)	(Opt.) Unsupervised learning: clustering & dimensionality reduction; evaluation intuition
12)	Reproducibility: experiment tracking, env/dependency management; (opt.) model registry awareness
13)	Model documentation and responsible reporting: model cards
14)	Sustainability in ML systems: technical debt risks, maintenance costs, production mindset
15)	Project workshop + review: baseline, improvements, final report/model card
16)	Final Exam

Sources

Course Notes / Textbooks:	Öğretim elemanı ders notları
References:

Relationship Between Course and Program Learning Outcomes

Course Learning Outcomes	1	2	3	4	5	6	7	8	9	10	11	12
Program Outcomes
1) Upon successful completion of this programme, students will be able to; Develop and apply deep theoretical and practical knowledge in specialized areas of computer engineering to complex engineering problems. Identify, model, and solve a problem in the field of computer engineering by using scientific research methods to develop original and innovative approaches. Analyze, synthesize, and evaluate new and complex ideas in the field with a critical perspective. Design and conduct an original research study independently, report the results in accordance with scientific ethics, and defend them in international platforms. Follow scientific and technological developments in the field of computer engineering and contribute to these advancements. Act with a sense of social, environmental, and ethical responsibility in their studies and research. Use at least one foreign language (English) to follow scientific developments in the field and communicate at an academic level, both in writing and orally.
1) Develop and apply deep theoretical and practical knowledge in specialized areas of computer engineering to complex engineering problems. Identify, model, and solve a problem in the field of computer engineering by using scientific research methods to develop original and innovative approaches. Analyze, synthesize, and evaluate new and complex ideas in the field with a critical perspective.
1) Design and conduct an original research study independently, report the results in accordance with scientific ethics, and defend them in international platforms. Follow scientific and technological developments in the field of computer engineering and contribute to these advancements.

Relationship Between Course and Learning Outcome

No Effect	1 Lowest	2 Low	3 Medium	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Upon successful completion of this programme, students will be able to; Develop and apply deep theoretical and practical knowledge in specialized areas of computer engineering to complex engineering problems. Identify, model, and solve a problem in the field of computer engineering by using scientific research methods to develop original and innovative approaches. Analyze, synthesize, and evaluate new and complex ideas in the field with a critical perspective. Design and conduct an original research study independently, report the results in accordance with scientific ethics, and defend them in international platforms. Follow scientific and technological developments in the field of computer engineering and contribute to these advancements. Act with a sense of social, environmental, and ethical responsibility in their studies and research. Use at least one foreign language (English) to follow scientific developments in the field and communicate at an academic level, both in writing and orally.
1)	Develop and apply deep theoretical and practical knowledge in specialized areas of computer engineering to complex engineering problems. Identify, model, and solve a problem in the field of computer engineering by using scientific research methods to develop original and innovative approaches. Analyze, synthesize, and evaluate new and complex ideas in the field with a critical perspective.
1)	Design and conduct an original research study independently, report the results in accordance with scientific ethics, and defend them in international platforms. Follow scientific and technological developments in the field of computer engineering and contribute to these advancements.

Learning Activities and Teaching Methods

Course

Assessment and Evaluation Methods and Criteria

Written Exam (open-ended questions, multiple-choice, true/false, matching, fill-in-the-blanks, ordering)
Homework

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 40
Final	1	% 60
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
Total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Duration (Hours)	Workload
Midterms	1	1	1
Final	1	1	1
Total Workload			2