CE 477 | Course Introduction and Application Information - Department of Mathematics

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.Öğretim Üyesi Alper Demir
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	The students who succeeded in this course; define computer tools to obtain, clean, and analyze data, apply statistical methods and visualization to explore data. use statistical and computational methods to make predictions from data. perform data analysis with machine learning methods, 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.
Related Sustainable Development Goals

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

Week	Subjects	Related Preparation
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	Association Mining	Chapter 4
8	Clustering	Chapter 4
9	Midterm Exam
10	Evaluation	Chapter 5
11	Ensemble Learning	Chapter 6
12	Extensions and Applications	Chapter 8
13	Extensions and Applications	Chapter 8
14	Project Presentations
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	Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project	1	30
Seminar / Workshop
Oral Exams
Midterm	1	30
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	1	24	24
Seminar / Workshop			0
Oral Exam			0
Midterms	1	25	25
Final Exam	1	25	25
		Total	150

#	Program Competencies/Outcomes	* Contribution Level
#	Program Competencies/Outcomes	1	2	3	4	5
1	To be able to have a grasp of basic mathematics, applied mathematics or theories and applications of statistics.	-	-	-	-	-
2	To be able to use advanced theoretical and applied knowledge, interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the fields of mathematics or statistics.	-	-	-	-	-
3	To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials.	-	-	-	-	-
4	To be able to evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach and develop positive attitude towards lifelong learning.	-	-	-	-	-
5	To be able to share the ideas and solution proposals to problems on issues in the field with professionals, non-professionals.	-	-	-	-	-
6	To be able to take responsibility both as a team member or individual in order to solve unexpected complex problems faced within the implementations in the field, planning and managing activities towards the development of subordinates in the framework of a project.	-	-	-	-	-
7	To be able to use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge.	-	-	-	-	-
8	To be able to act in accordance with social, scientific, cultural and ethical values on the stages of gathering, implementation and release of the results of data related to the field.	-	-	-	-	-
9	To be able to possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also environmental protection, worker's health and security.	-	-	-	-	-
10	To be able to connect concrete events and transfer solutions, collect data, analyze and interpret results using scientific methods and having a way of abstract thinking.	-	-	-	-	-
11	To be able to collect data in the areas of Mathematics or Statistics and communicate with colleagues in a foreign language.	-	-	-	-	-
12	To be able to speak a second foreign language at a medium level of fluency efficiently.	-	-	-	-	-
13	To be able to relate the knowledge accumulated throughout the human history to their field of expertise.	-	-	-	-	-