MATH 485 | Course Introduction and Application Information - Department of Mathematics

Course Name

Exploratory Data Analysis

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
MATH 485	Fall/Spring	3	0	3	8

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	First Cycle
Mode of Delivery	face to face
Teaching Methods and Techniques of the Course	Application: Experiment / Laboratory / Workshop Lecture / Presentation
National Occupation Classification	-
Course Coordinator	Doç. Dr. Sevin GÜMGÜM
Course Lecturer(s)	Doç. Dr. Özge Eren
Assistant(s)	Araş. Gör. Dr. Ayşe BELER Araş. Gör. Merve KAHRAMAN ARİMAN Araş. Gör. Sıla Selenay KOÇ

Course Objectives

The main objective of this course is to provide a basic understanding of data analysis concepts and to use it in applications with using some statistical software packages. The course will cover basic approaches in statistical inference and data mining, as well as modeling.

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	will be able to use graphic methods to describe and summarize data.
2	will be able to analyze the relationships between variables.
3	will be able to analyze the relationships between variables and regression models.
4	will be able to compare several audience averages.
5	will be able to create hypothesis tests for an audience.
6	will be able to use simple classification methods in data mining concepts.

Course Description

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

Week	Subjects	Related Preparation	Learning Outcome
1	Introduction to data analysis, data science, data scientist, data scientist’s toolbox, SPSS, introduction to R environment	R for Data Science, H. Wickham, G. Grolemund, (Ch-1, Ch-2), Introductory Statistics with R, P. Dalgaard (Ch-1)
2	Data structures in R, built-in functions, R packages	Introductory Statistics with R, P. Dalgaard (Ch-1)
3	Random data, density and distribution functions, data import and export, data manipulation	Introductory Statistics with R, P. Dalgaard (Ch-3)
4	Control structures, conditional statements	Introductory Statistics with R, P. Dalgaard (Ch-1.2)
5	Quantitative methods to describe data, relationships between several variables	Introductory Statistics with R, P. Dalgaard (Ch-4)
6	Data visualization, graphical methods to describe data, base graphics system in R, basic graphs	Introductory Statistics with R, P. Dalgaard (Ch-4.2)
7	Advanced graphics in R -1, tidyverse syntax, Advanced graphics in R -2, ggplot2	R for Data Science, H. Wickham, G. Grolemund, (Ch-3)
8	Midterm Exam
9	Hypothesis testing, one sample tests	Introductory Statistics with R, P. Dalgaard (Ch-5)
10	Hypothesis testing, two-sample tests	Introductory Statistics with R, P. Dalgaard (Ch-5)
11	Checking assumptions, goodness of fit tests	Introductory Statistics with R, P. Dalgaard (Ch-5)
12	Simple lineer regression and correlation	Introductory Statistics with R, P. Dalgaard (Ch-6)
13	Dynamic reporting	R for Data Science, H. Wickham, G. Grolemund, (Ch-27)
14	Data mining, basic concepts of statistical learning, supervised learning, unsupervised learning	R for Data Science, H. Wickham, G. Grolemund, (Ch-22)
15	Semester Review
16	Final Exam

Course Notes/Textbooks

1- Introductory Statistics with R, P. Dalgaard, Springer, 2008. ISBN-13: 978-0-387-79054-1. (https://link.springer.com/book/10.1007/978-0-387-79054-1#toc)

2- R for Data Science, H. Wickham, G. Grolemund, 978-1491910399. (https://r4ds.had.co.nz/)

Suggested Readings/Materials

1- R in Action: Data Analysis and Graphics with R. 2nd Ed., R. Kabacoff, 2015. 978-1617291388.

2- Practical Data Science with R, N. Zumel and J. Mount, Manning Publications, 2014. 9781617291562.

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5	LO 6
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury	1	10
Project	1	20
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade	3	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	3	42
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury	1	25	25
Project	1	40	40
Seminar / Workshop			0
Oral Exam			0
Midterms	1	40	40
Final Exam	1	45	45
		Total	240

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	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.		-	-	-	X	-
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.		-	-	-	-	X
3	To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials.		-	-	X	-	-
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.		-	-	X	-	-
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.		-	-	-	-	-