ENM5203 Statistical Data Analysis and Decision MakingBahçeşehir UniversityDegree Programs COMPUTER ENGINEERING (ENGLISH, PHD)General Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
COMPUTER ENGINEERING (ENGLISH, PHD)
PhD TR-NQF-HE: Level 8 QF-EHEA: Third Cycle EQF-LLL: Level 8

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
ENM5203 Statistical Data Analysis and Decision Making Fall 3 0 3 12
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: 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
Recommended Optional Program Components: N/A
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 Outcomes

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 / Textbooks: 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
Project 1 % 35
Midterms 1 % 25
Final 1 % 40
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
Study Hours Out of Class 14 4 56
Presentations / Seminar 1 15 15
Project 1 90 90
Midterms 1 40 40
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) Ability to identify and apply advanced concepts in computer engineering
2) Cooperate efficiently in intra-disciplinary and multi-disciplinary teams.
3) Have theoretical and practical basis in computer engineering and science to be able to conduct related academic research independently.
4) Ability to apply advanced mathematical and engineering knowledge on real problems.
5) Ability to search the scientific literature related to a research project and find the relationships with own research
6) Ability to interprete scientific research and use the findings
7) Ability to work efficiently in interdisciplinary research teams
8) Ability to attain scientific knowledge
9) Ability find ways to improve upon current knowledge
10) Ability to present research findings in seminars and conferences
11) Ability to write research progress reports by referring to published theses and papers.
12) Ability to show the responsibility of professional and ethical behavior