Psychology
Bachelor	Length of the Programme: 4	Number of Credits: 240	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF: Level 6

Ders Genel Tanıtım Bilgileri

School/Faculty/Institute

Faculty of Engineering

Course Code

DSAI 101

Course Title in English

Introduction to DS and AI

Course Title in Turkish

Veri Bilimi ve Yapay Zekaya Giriş

Language of Instruction

Type of Course

Guided Personal Study

Level of Course

Introductory

Semester

Fall

Contact Hours per Week

Lecture: 2

Recitation: 1

Lab: 0

Other: 0

Estimated Student Workload

156 hours per semester

Number of Credits

1 ECTS

Grading Mode

Pass / Fail

Pre-requisites

None

Expected Prior Knowledge

None

Co-requisites

None

Registration Restrictions

Only Undergraduate Students

Overall Educational Objective

To learn the fundamental concepts of Artificial Intelligence and Data Science and to become familiar with basic aspects of intelligent agents, knowledge representation, learning, and sensing.

Course Description

This course provides a comprehensive introduction to some fundamental aspects of Artificial Intelligence and Data Science. The following topics are covered: Introduction to Data Science, History and Future of Data Science, Big Data Analytics , Data Visualization, Introduction to Artificial Intelligence, Machine Learning, Neural Networks, Deep Learning, Natural Language Processing and some applications of Data Science and Artificial Intelligence.

Course Description in Turkish

Bu ders, Yapay Zeka ve Veri Biliminin bazı temel yönlerine kapsamlı bir giriş sağlar. Aşağıdaki konular ele alınmaktadır: Veri Bilimine Giriş, Veri Biliminin Tarihi ve Geleceği, Büyük Veri Analitiği, Veri Görselleştirme, Yapay Zekaya Giriş, Makine Öğrenimi, Sinir Ağları, Derin Öğrenme, Doğal Dil İşleme ve Veri Bilimi ve Yapay Zekanın bazı uygulamaları.

Course Learning Outcomes and Competences

Upon successful completion of the course, the learner is expected to be able to:
1) elaborate on the fundamental concepts related to artificial intelligence and data science while communicating effectively with a range of audiences via subject and case presentations;
2) recognize and discuss ethical and professional responsibilities in engineering situations that are directly related to artificial intelligence, data science and related technologies while considering the impact of engineering solutions in global, economic, environmental, and societal contexts;
3) acquire and demonstrate over a case related to contemporary issues in artificial intelligence and data processing/visualization etc. using appropriate learning strategies.

Program Learning Outcomes/Course Learning Outcomes	1	2	3
1) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology.
2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation.
3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes.
4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts.
5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline.
6) Internalization and dissemination of professional ethical standards.
7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences.
8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level).
9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity.
10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement.
11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses.
12) Ability to acquire knowledge independently, and to plan one’s own learning.
13) Demonstration of advanced competence in the clarity and composition of written work and presentations.

Relation to Program Outcomes and Competences

N None	S Supportive	H Highly Related

	Program Outcomes and Competences	Level	Assessed by
1)	Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology.	N
2)	Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation.	N
3)	Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes.	H	Exam,HW,Participation
4)	Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts.	N
5)	Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline.	N
6)	Internalization and dissemination of professional ethical standards.	N
7)	Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences.	N
8)	Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level).	N
9)	Recognition, understanding, and respect for the complexity of sociocultural and international diversity.	S	Participation
10)	Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement.	S	HW,Participation
11)	Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses.	N
12)	Ability to acquire knowledge independently, and to plan one’s own learning.	S	Exam,HW
13)	Demonstration of advanced competence in the clarity and composition of written work and presentations.	H	Exam,HW

Prepared by and Date	TUNA ÇAKAR , November 2023
Course Coordinator	TUNA ÇAKAR
Semester	Fall
Name of Instructor	Asst. Prof. Dr. TUNA ÇAKAR

Course Contents

Week	Subject
1)	What is Data Science?
2)	The Place of Data Science in the Data Universe
3)	Ethics and Agency
4)	Sources of Data
5)	Sources of Rules
6)	Tools for Data Science
7)	Mathematics for Data Science
8)	Unsupervised Learning
9)	Supervised Learning
10)	Generative Methods in Data Science, Acting on Data Science
11)	Artificial Intelligence and Business Strategy I
12)	Artificial Intelligence and Business Strategy II
13)	Finding Opportunities, Presenting Your Experience
14)	Building an Online Presence, Networking and Interviews
15)	Final Exam/Project/Presentation
16)	Final Exam/Project/Presentation

Required/Recommended Readings

Shared videos

Teaching Methods

Guided personal study

Homework and Projects

Presentation (%40), Final Exam (%60)

Laboratory Work

Computer Use

Required

Other Activities

Assessment Methods

Assessment Tools	Count	Weight
TOTAL		%

Course Administration

dsai@mef.edu.tr
05309225505
Instructor’s office: 5th floor Phone number: 0 212 395 37 50 Office hours: TBA E-mail address: cakart@mef.edu.tr

ECTS Student Workload Estimation

Activity	No/Weeks	Hours			Calculation
	No/Weeks per Semester	Preparing for the Activity	Spent in the Activity Itself	Completing the Activity Requirements
Course Hours	14	1	2		42
Laboratory	10	1	2		30
Study Hours Out of Class	1	1	10		11
Project	1	5	25		30
Homework Assignments	10	1	2		30
Final Examination	1	10	3		13
Total Workload					156
Total Workload/25					6.2
ECTS					1