INFORMATION TECHNOLOGIES (ENGLISH, NON-THESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
ENM5203 Statistical Data Analysis and Decision Making Spring 3 0 3 6
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Dr. Öğr. Üyesi ETHEM ÇANAKOĞLU
Course Lecturer(s): Prof. Dr. SELİM ZAİM
Dr. Öğr. Üyesi YÜCEL BATU SALMAN
Course Objectives: This course provides the use of statistical and quantitative methods using computer technology to examine and explore data. It aims to build and interpret models from this data for decision making in all functional areas.

Learning Outputs

The students who have succeeded in this course;
Ability to make decisions with statistical methods, ability to collect meaningful data, ability to interpret data and transform it into information/knowledge

Course Content

Methods covered include: collecting data, summarizing and exploring data, hypothesis testing, confidence intervals, regression analysis, ANOVA.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction: Description of data
2) Data collection
3) Descriptive Statistics
4) Confidence intervals
5) Hypothesis testing
6) Hypothesis testing
7) Midterm
8) Regression analysis
9) Regression analysis
10) ANOVA
12) ANOVA
12) SPSS
13) Project presentations
14) Project presentations

Sources

Course Notes: N/A
References: Statistics for Business: Decision Making and Analysis, Robert A. Stine, Dean Foster, Pearson, 2011, 0321123913

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance % 0
Laboratory % 0
Application % 0
Field Work % 0
Special Course Internship (Work Placement) % 0
Quizzes % 0
Homework Assignments % 0
Presentation % 0
Project 1 % 35
Seminar % 0
Midterms 1 % 25
Preliminary Jury % 0
Final 1 % 40
Paper Submission % 0
Jury % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 25
PERCENTAGE OF FINAL WORK % 75
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Laboratory 0 0 0
Application 0 0 0
Special Course Internship (Work Placement) 0 0 0
Field Work 0 0 0
Study Hours Out of Class 14 4 56
Presentations / Seminar 1 15 15
Project 1 90 90
Homework Assignments 0 0 0
Quizzes 0 0 0
Preliminary Jury 0 0 0
Midterms 1 40 40
Paper Submission 0 0 0
Jury 0 0 0
Final 1 60 60
Total Workload 303

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) Uses basic Software Engineering knowledge and competencies.
2) Applies the software development ability that is necessary for software engineering applications.
3) Uses data structures and applies information about algorithm development.
4) Develops system programs on operating systems.
5) Defines computer organization, design and architectures.
6) Creates the structure of computer networks and network security.
7) Uses business intelligence, data mining and data analysis tools, applies techniques about them.
8) Develops database applications and WEB based programs.
9) Defines, analyzes, designs and manages information technologies projects.
10) Uses and develops technology-based environments and tools in education.
11) Detects, identifies and solves information technology needs of the business environment.
12) Uses the capabilities of information technologies within the rules of professional responsibility and ethics.