Data Science for Engineers (IND368) | GSÜ

Content

Objective

The aim of this course is to teach industrial engineering students the fundamentals of data science, introduce methods for analyzing large datasets, and equip them with the skills to use data science methods in industrial applications.

Course Content

Week 1 – Introduction to Data Science: Definitions and Applications
Week 2 – Data Mining and Preprocessing Techniques
Week 3 – Statistical Data Analysis
Week 4 – Fundamentals of Machine Learning
Week 5 – Classification Models
Week 6 – Regression Analysis and Prediction Models
Week 7 – Clustering and Association Rules
Week 8 – Time Series Analysis
Week 9 – Midterm Exam
Week 10 – Fundamentals and Applications of Deep Learning
Week 11 – Natural Language Processing and Text Mining
Week 12 – Recommendation Systems and Applications
Week 13 – Big Data Technologies and Applications
Week 14 – Case Studies of Data Science in Industrial Applications

Course Learning Outcomes

A student who successfully completes this course will be able to:

1. Define the fundamental principles of data science methods.
2. Develop solutions to real-world problems by using different data science techniques and tools.
3. Decide in which situations advanced data science methods, such as machine learning and deep learning, can be used.
4. Interpret the results of data analysis and communicate the findings.
5. Integrate data science methods into decision support systems.
6. Plan and carry out data science projects.

Teaching and Learning Methods

Lectures and presentations, hands-on laboratory work, discussions

References

1. "Data Science for Business" - Foster Provost & Tom Fawcett
2. "Python for Data Analysis" - Wes McKinney
3. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" - Aurélien Géron
4. "The Art of Data Science" - Roger D. Peng & Elizabeth Matsui

Weekly Contents

Theory Topics
Week	Weekly Contents
1	Introduction to Data Science: 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	Midterm Exam
9	Time Series Analysis
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 of Data Science in Industrial Applications

Assessment System

Contribution to Overall Grade
Activities	Number	Contribution
Contribution of in-term studies to overall grade	7	60
Contribution of final exam to overall grade	1	40
Total	8	100

In-Term Studies
Activities	Number	Contribution
Assignments	5	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
Make-up	0	0
Total	7	60

Relation of Proficiency

No	Program Learning Outcomes	Contribution
No	Program Learning Outcomes	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

ECTS

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)	1	5	5
Project	1	20	20
Laboratory	0	0	0
Other Applications	0	0	0
Final Examinations (including preparation)	1	5	5
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
Yıl Sonu	0	0	0
Hazırlık Yıl Sonu	0	0	0
Hazırlık Bütünleme	0	0	0
Total Workload			96
Total Workload / 25			3.84
Credits ECTS			4