M.SC. in Computer Engineering (Without Thesis)

MATH 658 | Course Introduction and Application Information

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
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

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.

EVALUATION SYSTEM

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

ECTS / WORKLOAD TABLE

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

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.		-	-	-	-	X
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	-	-	X	-
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	-	-	-	X
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	-	X	-
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	X
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	-	-	X
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		-	X	-	-	-
8	Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility.		-	-	X	-	-
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		-	-	X	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		-	-	X	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.		-	-	X	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		-	X	-	-	-