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

IE 332

Course Title in English

Exploratory Data Analytics

Course Title in Turkish

Keşifsel Veri Analizi

Language of Instruction

Type of Course

Flipped Classroom,Lecture

Level of Course

Advanced

Semester

Spring

Contact Hours per Week

Lecture: 3

Recitation: none

Lab: none

Other: none

Estimated Student Workload

152 hours per semester

Number of Credits

6 ECTS

Grading Mode

Standard Letter Grade

Pre-requisites

None

Expected Prior Knowledge

Basic principles of computational methods and introductory level probability/statistics

Co-requisites

None

Registration Restrictions

none

Overall Educational Objective

To learn the basics of data analytics process with on hands applications using modern tools to explore data by summarizing, slicing/dicing and analyzing data via graphical and quantitative tools.

Course Description

The aim of the course is to give the fundamentals of exploratory data analytics. Exploratory data analytics focuses on exploring data to understand the data’s underlying structure and variables, to develop intuition about the data set, to consider how that data set came into existence, and to decide how it can be investigated with more formal statistical methods.

Course Description in Turkish

Bu ders veri analitiğinin temellerini inceleyen bir ders olarak tasarlanmıştır. Araştırma amaçlı veri analitiği verinin altında yatan yapılanmayı anlamaya, veri seti hakkında sezgi geliştirmeye, verinin nasıl ortaya çıkıp, nasıl hazırlandığını düşünmeye ve istatistiki metotlarla nasıl derinlemesine incelenebileceğine odaklanır.

Course Learning Outcomes and Competences

Upon successful completion of the course, the learner is expected to be able to:
1) summarize data using statistical methods;
2) draw conclusions and/or make predictions using data analytics;
3) apply linear regression models;
4) understand recent analytical trends such as text mining, recommendation systems;
5) communicate data analysis results in a clear and concise manner in written and oral form.

Program Learning Outcomes/Course Learning Outcomes	1	2	3	4	5
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	SEMRA AĞRALI , September 2023
Course Coordinator	SEMRA AĞRALI
Semester	Spring
Name of Instructor	Prof. Dr. SEMRA AĞRALI

Course Contents

Week	Subject
1)	Introduction to Data Science
2)	Basics of R
3)	Basics of R II
4)	Data Analysis (one variable)
5)	Data Analysis (two variable)
6)	Data Analysis (multi variable)
7)	Linear Regression
8)	Linear Regression II
9)	Linear Regression III
10)	Linear Regression IV
11)	Image Processing Basics
12)	Text Mining Basics
13)	Search Engines Basics
14)	Recommendation Engines Basics
15)	Final Exam/Project/Presentation period
16)	Final Exam/Project/Presentation period

Required/Recommended Readings

none

Teaching Methods

Lectures/contact hours using “flipped classroom” as an active learning technique

Homework and Projects

Final Project

Laboratory Work

None

Computer Use

Students will apply the methods they learned using R at the laboratory hours

Other Activities

none

Assessment Methods

Assessment Tools	Count	Weight
Quiz(zes)	3	% 36
Homework Assignments	12	% 20
Project	3	% 44
TOTAL		% 100

Course Administration

ozluko@mef.edu.tr
none
Instructor’s office and phone number: 5th Floor office hours: Thursdays 16:20-17:20 email address: ozluko@mef.edu.tr Rules for attendance: none Rules for late submission of assignments: It will be discounted 20/100 by each delayed day. Rules for missing a midterm: Provided that proper justification evidence is presented, each missed midterm exam will be given the grade of the final exam. There will be no make-up exams Missing a final: Faculty regulations A reminder of proper classroom behavior, code of student conduct: YÖK Regulations Statement on plagiarism: YÖK Regulations

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	3	2	84
Project	1	10	10		20
Quiz(zes)	3	3	1		12
Midterm(s)	1	12	2		14
Final Examination	1	20	2		22
Total Workload					152
Total Workload/25					6.1
ECTS					6