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- Software Engineering
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- Level of Qualification
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- Profile of the Program
- Program Outcomes
- Course & Program Outcomes Matrix
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- Program Structure
- Course Structure Diagram with Credits
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COURSE INTRODUCTION AND APPLICATION INFORMATION
se.cs.ieu.edu.tr
| Course Name | |
| Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
|---|---|---|---|---|---|
| Fall/Spring |
| Prerequisites | None | |||||
| Course Language | ||||||
| Course Type | Elective | |||||
| Course Level | - | |||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | Application: Experiment / Laboratory / Workshop | |||||
| Course Coordinator | - | |||||
| Course Lecturer(s) | ||||||
| Assistant(s) | ||||||
| Course Objectives | |
| Learning Outcomes | The students who succeeded in this course;
|
| Course Description |
|
| Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Managment Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Required Materials |
| 1 | Introduction to data analysis a) Data Science b) Data Scientist c) Data scientist’s toolbox d) SPSS e) Introduction to R environment (Installation, Editors) | Introduction (R for Data Science) Basics (Introductory Statistics with R) |
| 2 | Data Structures in R Built-in functions R packages | Basics, The R environment (Introductory Statistics with R) |
| 3 | Random data, density and distribution functions Data Import and Export Data Manipulation | Probability and distributions (Introductory Statistics with R) |
| 4 | Control Structures Conditional statements | The R environment (Introductory Statistics with R) |
| 5 | Quantitative methods to describe data Relationships between several variables | Descriptive statistics and graphics (Introductory Statistics with R) |
| 6 | Data Visualization Graphical methods to describe data Base graphics system in R, basic graphs | Descriptive statistics and graphics (Introductory Statistics with R) |
| 7 | Advanced graphics in R, ggplot2 | Data visualization (R for Data Science) |
| 8 | Hypothesis testing One sample tests | One- and two-sample tests (Introductory Statistics with R) |
| 9 | Hypothesis testing Two-sample tests Analysis of Variance | One- and two-sample tests (Introductory Statistics with R) |
| 10 | Nonparametric Test of Hypotheses, One Sample tests Goodness of Fit tests | One- and two-sample tests (Introductory Statistics with R) |
| 11 | Nonparametric Test of Hypotheses, Two sample tests k-samples tests | One- and two-sample tests Analysis of variance and the Kruskal–Wallis test (Introductory Statistics with R) |
| 12 | Linear regression models | Regression and correlation (Introductory Statistics with R) |
| 13 | Basics of Data Mining | Introduction (Data Mining: Concepts and Techniques) |
| 14 | Basics of Data Mining | Introduction (Data Mining: Concepts and Techniques) |
| 15 | Review of the Semester | |
| 16 | Review of the Semester |
| Course Notes/Textbooks | Lecture Notes Introductory Statistics with R, P. Dalgaard, Springer, 2008. |
| Suggested Readings/Materials | R for Data Science, H. Wickham, G. Grolemund, 2017. Practical Data Science with R, N. Zumel and J. Mount, Manning Publications, 2014. Data Mining: Concepts and Techniques, Han, M. Kamber, and J. Pei, Morgan Kaufmann, 2011. |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury | 1 | 10 |
| Project | 1 | 20 |
| Seminar / Workshop | ||
| Oral Exam | ||
| Midterm | 1 | 30 |
| Final Exam | 1 | 40 |
| Total |
| Weighting of Semester Activities on the Final Grade | 8 | 60 |
| Weighting of End-of-Semester Activities on the Final Grade | 1 | 40 |
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Course Hours (Including exam week: 16 x total hours) | 16 | 3 | 48 |
| Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | ||
| Study Hours Out of Class | 12 | 2 | |
| Field Work | |||
| Quizzes / Studio Critiques | |||
| Portfolio | |||
| Homework / Assignments | |||
| Presentation / Jury | 1 | 16 | |
| Project | 1 | 20 | |
| Seminar / Workshop | |||
| Oral Exam | |||
| Midterms | 1 | 20 | |
| Final Exams | 1 | 30 | |
| Total | 158 |
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
| # | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
| 1 | Be able to define problems in real life by identifying functional and nonfunctional requirements that the software is to execute | |||||
| 2 | Be able to design and analyze software at component, subsystem, and software architecture level | |||||
| 3 | Be able to develop software by coding, verifying, doing unit testing and debugging | |||||
| 4 | Be able to verify software by testing its behaviour, execution conditions, and expected results | |||||
| 5 | Be able to maintain software due to working environment changes, new user demands and the emergence of software errors that occur during operation | |||||
| 6 | Be able to monitor and control changes in the software, the integration of software with other software systems, and plan to release software versions systematically | |||||
| 7 | To have knowledge in the area of software requirements understanding, process planning, output specification, resource planning, risk management and quality planning | |||||
| 8 | Be able to identify, evaluate, measure and manage changes in software development by applying software engineering processes | |||||
| 9 | Be able to use various tools and methods to do the software requirements, design, development, testing and maintenance | |||||
| 10 | To have knowledge of basic quality metrics, software life cycle processes, software quality, quality model characteristics, and be able to use them to develop, verify and test software | |||||
| 11 | To have knowledge in other disciplines that have common boundaries with software engineering such as computer engineering, management, mathematics, project management, quality management, software ergonomics and systems engineering | |||||
| 12 | Be able to grasp software engineering culture and concept of ethics, and have the basic information of applying them in the software engineering | |||||
| 13 | Be able to use a foreign language to follow related field publications and communicate with colleagues | |||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest