SOFTWARE ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
EEE5022	Applied Statistics	Spring	3	0	3	9

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Assoc. Prof. SAEID KARAMZADEH
Course Lecturer(s):	Prof. Dr. SELİM ZAİM
Recommended Optional Program Components:	none..........
Course Objectives:	The course introduces fundamental topics in statistics and implements its applications to industrial, medical, financial, energy and similar type very large-size datasets to infer meaninful statistical results. The course is for gradute students with no significant background on this subject. Implementations will be performed on the open source statistical software R. Introduction to R programming will be given.

Learning Outcomes

The students who have succeeded in this course;
I. Identify basic terms in statistics.
II. Gain ability to use and apply basic methods and programming tools used in statistics over various engineering disciplines.
III. Ability to explore data and its relationships.
IV. Ability to perform hypothesis testing for statistical problems.
V. Perform statistical inference over statistical data.

Course Content

Topics include: Introduction to R programming, Sampling, Data Exploration, Exploring Relationships, Probability, Random Variables and Probability Distributions, Estimation, Hypothesis Testing, Statistical Inference, Multiple Testing Correction, ANOVA, Analysis of Categorical Variables, Regression Analysis, Bayesian Analysis, Survival Analysis, Over Representation Analysis, Meta Analysis.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction
2)	Introduction to R statistical programming
3)	Term Project
4)	Data Exploration with R
5)	Visualizing and Summarizing Relationships
6)	Probability and Random Variables
7)	Estimation in datasets
8)	Hypothesis Testing for various engineering applications
9)	Statistical Inference over various large datasets
10)	ANOVA
11)	Analysis of Categorical Variables
12)	Regression and Bayesian Analysis
13)	Survival analysis
14)	Over Representation Analysis

Sources

Course Notes / Textbooks:	Principles of Applied Statistics (Paperback), by D. R. Cox, Christl A. Donnelly 2011 ISBN-10: 1107644453 \| ISBN-13: 978-1107644458
References:	Introductory Statistics with R Peter Dalgaard 2011 ISBN 978-0-387-79053-4

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Project	1	% 30
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 30
PERCENTAGE OF FINAL WORK		% 70
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Workload
Course Hours	14	42
Study Hours Out of Class	14	42
Project	1	30
Midterms	1	40
Final	1	50
Total Workload		204

Contribution of Learning Outcomes to Programme Outcomes

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

	Program Outcomes	Level of Contribution
1)	Be able to specify functional and non-functional attributes of software projects, processes and products.
2)	Be able to design software architecture, components, interfaces and subcomponents of a system for complex engineering problems.
3)	Be able to develop a complex software system with in terms of code development, verification, testing and debugging.
4)	Be able to verify software by testing its program behavior through expected results for a complex engineering problem.
5)	Be able to maintain a complex software system due to working environment changes, new user demands and software errors that occur during operation.
6)	Be able to monitor and control changes in the complex software system, to integrate the software with other systems, and to plan and manage new releases systematically.
7)	Be able to identify, evaluate, measure, manage and apply complex software system life cycle processes in software development by working within and interdisciplinary teams.
8)	Be able to use various tools and methods to collect software requirements, design, develop, test and maintain software under realistic constraints and conditions in complex engineering problems.
9)	Be able to define basic quality metrics, apply software life cycle processes, measure software quality, identify quality model characteristics, apply standards and be able to use them to analyze, design, develop, verify and test complex software system.
10)	Be able to gain technical information about other disciplines such as sustainable development that have common boundaries with software engineering such as mathematics, science, computer engineering, industrial engineering, systems engineering, economics, management and be able to create innovative ideas in entrepreneurship activities.
11)	Be able to grasp software engineering culture and concept of ethics and have the basic information of applying them in the software engineering and learn and successfully apply necessary technical skills through professional life.
12)	Be able to write active reports using foreign languages and Turkish, understand written reports, prepare design and production reports, make effective presentations, give clear and understandable instructions.
13)	Be able to have knowledge about the effects of engineering applications on health, environment and security in universal and societal dimensions and the problems of engineering in the era and the legal consequences of engineering solutions.