Engineering Data Analytics(IND363)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| IND363 | Engineering Data Analytics | 5 | 3 | 0 | 0 | 4 | 4 |
| Prerequisites | ING231/ING242 |
| Admission Requirements | ING231/ING242 |
| Language of Instruction | French |
| Course Type | Elective |
| Course Level | Bachelor Degree |
| Course Instructor(s) | Sadettin Emre ALPTEKİN ealptekin@gsu.edu.tr (Email) |
| Assistant | |
| Objective | The objective of this course is to teach industrial engineering students the fundamentals of data analytics, introduce methods for analyzing large datasets, and equip students with skills to apply data analytics techniques for industrial applications. |
| Content |
1. Week - Introduction to Data Analytics: Definitions and Applications 2. Week - Data Mining and Preprocessing Techniques 3. Week - Statistical Data Analysis 4. Week - Fundamentals of Machine Learning 5. Week - Classification Models 6. Week - Regression Analysis and Prediction Models 7. Week - Clustering and Association Rules 8. Week - Time Series Analysis 9. Week - Midterm Exam 10. Week - Fundamentals and Applications of Deep Learning 11. Week - Natural Language Processing and Text Mining 12. Week - Recommendation Systems and Applications 13. Week - Big Data Technologies and Applications 14. Week - Case Studies in Data Analytics for Industrial Applications |
| Course Learning Outcomes |
- Understand the basic principles of data analytics methods. - Gain the ability to generate solutions for real-world problems using various data analytics techniques and tools. - Capability to analyze industrial datasets. - Have basic knowledge about advanced data analytics methods (machine learning, deep learning, etc.). - Interpret data analytics results and communicate outcomes. - Integrate data analytics methods for decision support systems. - Plan and execute data analytics projects. |
| Teaching and Learning Methods | Lecture and presentation, applied laboratory work, discussions |
| References |
"Data Science for Business" - Foster Provost & Tom Fawcett "Python for Data Analysis" - Wes McKinney "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" - Aurélien Géron "The Art of Data Science" - Roger D. Peng & Elizabeth Matsui "Coursera" platform courses |
Theory Topics
| Week | Weekly Contents |
|---|---|
| 1 | Introduction to Data Analytics: Definitions and Applications |
| 2 | Data Mining and Preprocessing Techniques |
| 3 | Statistical Data Analysis |
| 4 | Fundamentals of Machine Learning |
| 5 | Classification Models |
| 6 | Regression Analysis and Prediction Models |
| 7 | Clustering and Association Rules |
| 8 | Time Series Analysis |
| 9 | Midterm Exam |
| 10 | Fundamentals and Applications of Deep Learning |
| 11 | Natural Language Processing and Text Mining |
| 12 | Recommendation Systems and Applications |
| 13 | Big Data Technologies and Applications |
| 14 | Case Studies in Data Analytics for Industrial Applications |
Practice Topics
| Week | Weekly Contents |
|---|---|
| 1 | |
| 2 | |
| 3 | |
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| 5 | |
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| 7 | |
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| 9 | |
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| 13 | |
| 14 |
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 1 | 60 |
| Contribution of final exam to overall grade | 1 | 40 |
| Toplam | 2 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 4 | 15 |
| Presentation | 0 | 0 |
| Midterm Examinations (including preparation) | 1 | 25 |
| Project | 1 | 20 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 0 | 0 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 0 | 0 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Toplam | 6 | 60 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Knowledge and understanding of a wide range of basic sciences (math, physics, ...) and the main concepts of engineering | X | ||||
| 2 | Ability to combine the knowledge and skills to solve engineering problems and provide reliable solutions | X | ||||
| 3 | Ability to select and apply methods of analysis and modeling to ask, reformulate and solve the complex problems of industrial engineering | X | ||||
| 4 | Ability to conceptualize complex systems, processes or products under practical constraints to improve their performance, ability to use innovative methods of design | X | ||||
| 5 | Ability to design, select and apply methods and tools needed to solve problems related to the practice of industrial engineering, ability to use computer technology | X | ||||
| 6 | Ability to design experiments, collect and interpret data and analyze results | X | ||||
| 7 | Ability to work independently, ability to participate in working groups and have a multidisciplinary team spirit | |||||
| 8 | Ability to communicate effectively, ability to speak at least two foreign languages | |||||
| 9 | Awareness of the need for continuous improvement of lifelong learning, ability to keep abreast of scientific and technological developments to use the tools of information management | |||||
| 10 | Awareness of professional and ethical responsibility | |||||
| 11 | Knowledge of the concepts of professional life as "project management", "risk management" and "management of change" | |||||
| 12 | Knowledge on entrepreneurship, innovation and sustainability | |||||
| 13 | Understanding of the effects of Industrial Engineering applications on global and social health, environment and safety. | |||||
| 14 | Knowledge of the problems of contemporary society | |||||
| 15 | Knowledge of the legal implications of the practice of industrial engineering | |||||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 14 | 3 | 42 |
| Working Hours out of Class | 14 | 1 | 14 |
| Assignments | 5 | 2 | 10 |
| Presentation | 0 | 0 | 0 |
| Midterm Examinations (including preparation) | 0 | 0 | 0 |
| Project | 1 | 20 | 20 |
| Laboratory | 0 | 0 | 0 |
| Other Applications | 0 | 0 | 0 |
| Final Examinations (including preparation) | 1 | 14 | 14 |
| Quiz | 0 | 0 | 0 |
| Term Paper/ Project | 0 | 0 | 0 |
| Portfolio Study | 0 | 0 | 0 |
| Reports | 0 | 0 | 0 |
| Learning Diary | 0 | 0 | 0 |
| Thesis/ Project | 0 | 0 | 0 |
| Seminar | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Make-up | 0 | 0 | 0 |
| Total Workload | 100 | ||
| Total Workload / 25 | 4.00 | ||
| Credits ECTS | 4 | ||