Manufacturing Execution Systems Operator
Course Details
KTO KARATAY UNIVERSITY
Trade and Industry Vocational School
Programme of Manufacturing Execution Systems Operator
Course Details
Trade and Industry Vocational School
Programme of Manufacturing Execution Systems Operator
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 07831112 | Data Collection and Analysis | 2 | Autumn | 3 | 2+2+0 | 5 | 5 |
| Course Type | Elective |
| Course Cycle | Associate (Short Cycle) (TQF-HE: Level 5 / QF-EHEA: Short Cycle / EQF-LLL: Level 5) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | Lect. Mehmet AKSOY |
| Instructor(s) | Lect. Hacer TAŞDÖĞEN |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Lect. Hacer TAŞDÖĞEN | T-219 | [email protected] | 7409 | Wednesday 10:00-12:00 |
Course Content
This course will cover the definition and importance of data, data collection systems, data filtering and cleaning, data storage methods, data modeling, data distributions, data analysis, and analytical methods such as time series analysis, regression analysis, and machine learning techniques. It will also include practical applications on interpreting and extracting meaningful insights from data.
Objectives of the Course
The course aims to teach the definition and importance of data, data collection systems, data filtering and cleaning, data storage methods, data modeling, data distributions, and data analysis techniques.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | X |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P1 | Possesses fundamental, up-to-date, and practical knowledge related to their profession | 3 |
| P4 | Effectively uses information technologies (software, programs, animation, etc.) related to their profession | 5 |
| P7 | Takes responsibility as a team member to resolve complex and unforeseen issues encountered in applications related to the field. | 4 |
| P9 | The collection, application, and dissemination of data related to the field are guided by social, scientific, cultural, and ethical values. | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Knows data types, their types and the differences between them. | P.1.18 | 1 |
| O2 | Understands data structures and organizational processes. | P.1.19 | 1 |
| O3 | Have the ability to model and analyze data. | P.4.27 | 1,3 |
| O4 | Can select appropriate technological and statistical approaches for data analysis. | P.4.28 | 1 |
| O5 | Interprets the analysis results and relates them to decision processes. | P.7.6 | 1 |
| O6 | Explains the processes of collecting, extracting, cleaning and transforming data. | P.9.6 | 1 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Basic Concepts of Data Collection and Analysis |
| 2 | Data Collection Methods and Approaches |
| 3 | Data Structures, Types, and Organization |
| 4 | Methods for Data Type Conversion |
| 5 | Creating a Dataset |
| 6 | Data Filtering and Cleaning |
| 7 | Data Storage Methods and Data Management |
| 8 | Statistical Methods in Data Analysis |
| 9 | Data Analysis and Analytical Methods Using Data Mining and Machine Learning Algorithms |
| 10 | Practical Example of Dataset Creation |
| 11 | Extracting the Most Informative Minimal Data (Quality Data) from a Dataset – Data Selection Practice |
| 12 | Exploring Suitable Decision Support Models for Data |
| 13 | Modeling and Interpreting Data for Autonomous Systems |
| 14 | Ethical Rules and Responsibilities in Data Acquisition and Usage |
Textbook or Material
| Resources | Research Design: Qualitative, Quantitative, and Mixed Methods Approaches – John W. Creswell |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Homework | 1 | 15 (%) |
| Presentation | - | - |
| Projects | - | - |
| Quiz | - | - |
| Midterms | 1 | 35 (%) |
| Final Exam | 1 | 50 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 4 | 56 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 4 | 56 |
| Midterms | 1 | 8 | 8 |
| Quiz | 0 | 0 | 0 |
| Homework | 1 | 10 | 10 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 1 | 8 | 8 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 12 | 12 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 150 | ||
| Total Work Load / 30 | 5 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P4 | P7 | P9 |
|---|---|---|---|---|---|
| O1 | Knows data types, their types and the differences between them. | 3 | - | - | - |
| O2 | Understands data structures and organizational processes. | 4 | - | - | - |
| O3 | Have the ability to model and analyze data. | - | 5 | - | - |
| O4 | Can select appropriate technological and statistical approaches for data analysis. | - | 4 | - | - |
| O5 | Interprets the analysis results and relates them to decision processes. | - | - | 4 | - |
| O6 | Explains the processes of collecting, extracting, cleaning and transforming data. | - | - | - | 5 |