BİLİŞİM GÜVENLİĞİ TEKNOLOJİSİ
Associate	TR-NQF-HE: Level 5	QF-EHEA: Short Cycle	EQF-LLL: Level 5

Ders Genel Tanıtım Bilgileri

Course Code:

2000002015

Ders İsmi:

Data Analysıs

Ders Yarıyılı:

Fall

Ders Kredileri:

Theoretical	Practical	Credit	ECTS
3	0	3	3

Language of instruction:

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Type of course:

Bölüm Seçmeli

Course Level:

Associate

TR-NQF-HE:5. Master`s Degree

QF-EHEA:Short Cycle

EQF-LLL:5. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Öğr.Gör. Esma TAYRAN

Course Lecturer(s):

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:	To perform statistical analysis and interpretation of data with the help of various package programs (MATLAB and Python) by teaching the basic concepts and methods of data analysis.
Course Content:	Course Content Data structures, types and organization, determining the suitability of data for parametric and non-parametric methods and models, methods of reaching complete information about the population with data usage

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
2 - Skills
Cognitive - Practical
1) Has advanced theoretical and applied knowledge in the fields of mathematics or computer science.
2) Interprets and evaluates data using the knowledge and skills acquired in the fields of mathematics or computer science.
3) Identifies, defines and analyzes problems in the fields of mathematics or computer science; develops solutions based on research and evidence.
4) By having the discipline of mathematics, he or she understands the logic of the computer and gains the ability to think based on calculations.
5) He uses time effectively in the process of drawing conclusions with his or her analytical thinking ability.
3 - Competences
Communication and Social Competence
1) He uses his or her knowledge about his field for the benefit of society.
Learning Competence
Field Specific Competence
1) Works effectively individually and as a team member to solve problems encountered in the fields of mathematics or computer science.
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week	Subject	Related Preparation
1)	Introduction to Data Science: The current place of statistics	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar
2)	Introduction to Statistics: Basic concepts, Data Collection and Sampling Methods	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar
3)	Descriptive statistics: Frequency Distribution, Central Tendency and Measures of Variability	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar
4)	Statistical Estimation and Hypothesis Testing	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar
5)	Simple and Partial Correlation, Parametric Hypothesis Tests: T-Test and Z-Test	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar
6)	Non-Parametric Hypothesis Tests: Chi-Square, Mann Whitney U	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar
7)	SPSS and Python Applications	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar
8)	Midterm	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar
9)	Analysis of Variance (ANOVA) and Kruskal-Wallis Tests	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar
10)	Simple/Multiple Linear Regression and Correlation Analysis	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar
11)	Project 1: SPSS and Python Applications	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar
12)	Factor Analysis	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar
13)	Cluster Analysis	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar
14)	Project 2: Introduction to Machine Learning: MATLAB and Python Applications	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar
15)	Project Presentations	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar

Sources

Course Notes / Textbooks:	Dersin yürütücüsü tarafından notlar paylaşılacaktır.
References:	An Introduction to Statistical Methods and Data Analysis, R.Lyman Ott, Micheal T. Longneckar Biostatistics with R, An Introduction to Statistics Through Biological Data, Babak Shahbaba

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	2	3	4	5	6	7
Program Outcomes
1) Having knowledge and skills in security algorithms for programming
2) Ability to install and manage software required for end user security
3) Having the ability to install and manage computer networks and use the network operating system
4) Have basic database and web programming skills

Ders - Öğrenme Kazanımı İlişkisi

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Having knowledge and skills in security algorithms for programming	2
2)	Ability to install and manage software required for end user security	2
3)	Having the ability to install and manage computer networks and use the network operating system	2
4)	Have basic database and web programming skills	2

Öğrenme Etkinliği ve Öğretme Yöntemleri

Alan Çalışması
Anlatım
Bireysel çalışma ve ödevi
Course
Grup çalışması ve ödevi
Web Tabanlı Öğrenme

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Sözlü sınav
Homework
Grup Projesi
Sunum

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 40
Semester Final Exam	1	% 60
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	2	28
Study Hours Out of Class	14	2	28
Midterms	1	10	10
Final	1	10	10
Total Workload			76