MATH 658 | Course Introduction and Application Information - PH.D. In Applied Mathematics and Statistics

Course Name

Advanced Data Analysis

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
MATH 658	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Third Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	Application: Experiment / Laboratory / Workshop Lecture / Presentation
National Occupation Classification	-
Course Coordinator	Prof. Dr. Gözde Yazgı TÜTÜNCÜ
Course Lecturer(s)	Dr. Öğr. Üyesi Engin YILDIZTEPE
Assistant(s)	-

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 graphical methods to describe and summarize data
2	will be able to analyze relationships between variables
3	will be able to model relationships between variables using regression models
4	will be able to compare population means
5	will be able to test hypotheses
6	will be able to discuss the basic concepts of Data Mining

Course Description

Course Category	Core Courses
	Major Area Courses	X
	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, two-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	4	56
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury	1	23	23
Project	1	28	28
Seminar / Workshop			0
Oral Exam			0
Midterms	1	30	30
Final Exam	1	40	40
		Total	225

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	To develop and deepen his/her knowledge on theories of mathematics and statistics and their applications in level of expertise, and to obtain unique definitions which bring innovations to the area, based on master level competencies,		-	-	-	-	X
2	To have the ability of original, independent and critical thinking in Mathematics and Statistics and to be able to develop theoretical concepts,		-	-	-	X	-
3	To have the ability of defining and verifying problems in Mathematics and Statistics,		-	-	X	-	-
4	With an interdisciplinary approach, to be able to apply theoretical and applied methods of mathematics and statistics in analyzing and solving new problems and to be able to discover his/her own potentials with respect to the application,		-	-	-	-	-
5	In nearly every fields that mathematics and statistics are used, to be able to execute, conclude and report a research, which requires expertise, independently,		-	-	-	-	-
6	To be able to evaluate and renew his/her abilities and knowledge acquired in the field of Applied Mathematics and Statistics with critical approach, and to be able to analyze, synthesize and evaluate complex thoughts in a critical way,		-	-	-	-	-
7	To be able to convey his/her analyses and methods in the field of Applied Mathematics and Statistics to the experts in a scientific way,		-	-	X	-	-
8	To be able to use national and international academic resources (English) efficiently, to update his/her knowledge, to communicate with his/her native and foreign colleagues easily, to follow the literature periodically, to contribute scientific meetings held in his/her own field and other fields systematically as written, oral and visual.		-	-	-	-	-
9	To be familiar with computer software commonly used in the fields of Applied Mathematics and Statistics and to be able to use at least two of them efficiently,		-	-	-	-	-
10	To contribute the transformation process of his/her own society into an information society and the sustainability of this process by introducing scientific, technological, social and cultural advances in the fields of Applied Mathematics and Statistics,		-	-	-	-	-
11	As having rich cultural background and social sensitivity with a global perspective, to be able to evaluate all processes efficiently, to be able to contribute the solutions of social, scientific, cultural and ethical problems and to support the development of these values,		-	-	-	-	-
12	As being competent in abstract thinking, to be able to connect abstract events to concrete events and to transfer solutions, to analyze results with scientific methods by designing experiment and collecting data and to interpret them,		-	-	-	-	-
13	To be able to produce strategies, policies and plans about systems and topics in which mathematics and statistics are used and to be able to interpret and develop results,		-	-	-	-	-
14	To be able to evaluate, argue and analyze prominent persons, events and phenomena, which play an important role in the development and combination of the fields of Mathematics and Statistics, within the perspective of the development of other fields of science,		-	-	-	-	-
15	In Applied Mathematics and Statistics, to be able to sustain scientific work as an individual or a group, to be effective in all phases of an independent work, to participate decision-making process and to make and execute necessary planning within an effective time schedule.		-	-	-	-	-