CE 477 | Course Introduction and Application Information - Department of Software Engineering

Course Name

Data Science

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 477	Fall/Spring	3	0	3	5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	First Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	Group Work Problem Solving Lecture / Presentation
National Occupation Classification	-
Course Coordinator	Dr. Öğr. Üyesi Alper Demir
Course Lecturer(s)	Dr. Öğr. Üyesi İbrahim ZİNCİR
Assistant(s)	-

Course Objectives

The course introduces the principles and methods of data science – learning from data for prediction and insight. The course covers the key data science topics including getting data, visualizing and exploring data, statistical analysis of data, and the data science’s use of machine learning. The course focuses on developing hands-on data skills by offering the students to complete a data science project.

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	define computer tools to obtain, clean, and analyze data,
2	apply statistical methods and visualization to explore data.
3	use statistical and computational methods to make predictions from data.
4	perform data analysis with machine learning methods,
5	use statistics and data visualization tools to communicate the outcome of data analyses.

Course Description

The following topics will be included: getting and cleaning data, exploring data, statistical models of data, statistical inference, main machine learning methods in data science including linear regression, SVM, k-nearest neighbors, Naïve Bayes, logistic regression, decision trees, random forests, clustering, and dimensionality reduction, over-fitting, cross-validation, feature engineering.

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation	Learning Outcome
1	Introduction	Chapter 1
2	Input: Concepts, instances, attributes	Chapter 2
3	Output: Knowledge representation	Chapter 3
4	Data Visualization and Preprocessing	Chapter 7
5	Classification and Regression	Chapter 4
6	Time Series Analysis	Chapter 4
7	Midterm Exam I
8	Association Mining	Chapter 4
9	Clustering	Chapter 4
10	Evaluation	Chapter 5
11	Ensemble Learning	Chapter 6
12	Extensions and Applications	Chapter 8
13	Extensions and Applications	Chapter 8
14	Midterm Exam II
15	Semester review
16	Final Exam

Course Notes/Textbooks

I. E. Witten et al, “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, 2016, ISBN 978-0128042915

Suggested Readings/Materials

J. Grus, “Data Science from Scratch: First Principles with Python”, O’Reilly Media, 2015, ISBN 9781491901427- 9781491904381 (Ebook);

T. Hastie, R. Tibshirani, J. Friedman “The Elements of Statistical Learning”, Springer, 2013, ISBN 9780387216065;

S. Raschka, “Python Machine Learning”, Packt Publishing, 2015, ISBN 9781783555147;

R. D. Peng, E. Matsui, “The Art of Data Science”, https://leanpub.com/artofdatascience

Han, Jiawei, Jian Pei, and Hanghang Tong. Data mining: concepts and techniques. Morgan kaufmann, 2022.

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm	2	60
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade	2	60
Weighting of End-of-Semester Activities on the Final Grade	1	40
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	14	2	28
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury			0
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms	2	20	40
Final Exam	1	24	24
		Total	140

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computer computation, and topics specific to related engineering disciplines; the ability to use this knowledge in solving complex engineering problems		-	-	-	-	-
	1	Mathematics	-	-	-	-	-
	2	Science	-	-	-	-	-
	3	Basic engineering	-	-	-	-	-
	4	Computer computation	-	-	-	-	-
	5	Topics specific to related engineering disciplines	-	-	-	-	-
	6	The ability to use this knowledge in solving complex engineering problems	-	-	-	-	-
2	Problem Analysis: The ability to define, formulate, and analyze complex engineering problems by using fundamental science, mathematics, and engineering knowledge, while considering the relevant UN Sustainable Development Goals (SDGs) related to the problem.		-	-	-	X	-
3	Engineering Design: The ability to design creative solutions to complex engineering problems; the ability to design complex systems, processes, devices, or products that meet present and future requirements, considering realistic constraints and conditions.		-	-	-	-	-
	1	The ability to design creative solutions to complex engineering problems	-	-	-	-	-
	2	Considering realistic constraints and conditions in designing complex systems, processes, devices, or products	-	-	-	-	-
	3	The ability to design in a way that meets current and future requirements	-	-	-	-	-
4	Use of Techniques and Tools: The ability to select and use appropriate techniques, resources, and modern engineering and information technology tools, including prediction and modeling, for the analysis and solution of complex engineering problems, while being aware of their limitations		-	-	-	X	-
5	Research and Investigation: The ability to use research methods, including literature review, designing experiments, conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems.		-	-	-	X	-
	1	The ability to use research methods, including literature review	-	-	-	-	-
	2	Designing experiments	-	-	-	-	-
	3	Conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems	-	-	-	-	-
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs); awareness of the legal consequences of engineering solutions		-	-	-	-	-
	1	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs)	-	-	-	-	-
	2	Awareness of the legal consequences of engineering solutions	-	-	-	-	-
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility; awareness of acting impartially and inclusively, without discrimination in any matter. (FENG101)		-	-	-	-	-
	1	Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility	-	-	-	-	-
	2	Awareness of acting impartially and inclusively, without discrimination in any matter.	-	-	-	-	-
8	Individual and Team Work: The ability to work effectively as an individual and as a member or leader of both intra-disciplinary and interdisciplinary teams (whether face-to-face, remote, or hybrid).		-	-	-	-	-
9	Verbal and Written Communication: Taking into account the various differences of the target audience (such as education, language, profession), particularly in technical matters.		-	-	-	-	-
	1	Verbal (ENGxxx)	-	-	-	-	-
	2	Written effective communication skills. (ENGxxx)	-	-	-	-	-
10	Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.		-	-	-	-	-
	1	Knowledge of business practices such as project management and economic feasibility analysis; (FENG497-FENG498)	-	-	-	-	-
	2	Awareness of entrepreneurship and innovation. (FENG101)	-	-	-	-	-
11	Lifelong Learning: The ability to learn independently and continuously, adapt to new and emerging technologies, and think critically about technological changes.		-	-	-	-	-

CE 477 | Course Introduction and Application Information

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