FACULTY OF ENGINEERING
Department of Industrial Engineering
SOCIAL MEDIA
CE 477 | Course Introduction and Application Information
| Course Name |
Data Science
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
CE 477
|
Fall/Spring
|
3
|
0
|
3
|
5
|
| Prerequisites |
None
|
|||||
| Course Language |
English
|
|||||
| Course Type |
Elective
|
|||||
| Course Level |
First Cycle
|
|||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | Group WorkProblem SolvingLecture / Presentation | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | The course introduces the principles and methods of data science – learning from data for prediction and insight. The course covers the key data science topics including getting data, visualizing and exploring data, statistical analysis of data, and the data science’s use of machine learning. The course focuses on developing hands-on data skills by offering the students to complete a data science project. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | The following topics will be included: getting and cleaning data, exploring data, statistical models of data, statistical inference, main machine learning methods in data science including linear regression, SVM, k-nearest neighbors, Naïve Bayes, logistic regression, decision trees, random forests, clustering, and dimensionality reduction, over-fitting, cross-validation, feature engineering. |
|
|
Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | Introduction | Chapter 1 |
| 2 | Input: Concepts, instances, attributes | Chapter 2 |
| 3 | Output: Knowledge representation | Chapter 3 |
| 4 | Data Visualization and Preprocessing | Chapter 7 |
| 5 | Classification and Regression | Chapter 4 |
| 6 | Time Series Analysis | Chapter 4 |
| 7 | Association Mining | Chapter 4 |
| 8 | Midterm Exam | - |
| 9 | Clustering | Chapter 4 |
| 10 | Evaluation | Chapter 5 |
| 11 | Ensemble Learning | Chapter 6 |
| 12 | Extensions and Applications | Chapter 8 |
| 13 | Extensions and Applications | Chapter 8 |
| 14 | Project presentations | |
| 15 | Semester review | |
| 16 | Final Exam |
| Course Notes/Textbooks | I. E. Witten et al, “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, 2016, ISBN 978-0128042915 |
| Suggested Readings/Materials | J. Grus, “Data Science from Scratch: First Principles with Python”, O’Reilly Media, 2015, ISBN 9781491901427- 9781491904381 (Ebook); T. Hastie, R. Tibshirani, J. Friedman “The Elements of Statistical Learning”, Springer, 2013, ISBN 9780387216065; S. Raschka, “Python Machine Learning”, Packt Publishing, 2015, ISBN 9781783555147; R. D. Peng, E. Matsui, “The Art of Data Science”, https://leanpub.com/artofdatascience Han, Jiawei, Jian Pei, and Hanghang Tong. Data mining: concepts and techniques. Morgan kaufmann, 2022. |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments |
1
|
15
|
| Presentation / Jury | ||
| Project |
1
|
15
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
30
|
| Final Exam |
1
|
40
|
| Total |
| Weighting of Semester Activities on the Final Grade |
3
|
60
|
| Weighting of End-of-Semester Activities on the Final Grade |
1
|
40
|
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
| Study Hours Out of Class |
14
|
2
|
28
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
3
|
5
|
15
|
| Presentation / Jury |
0
|
||
| Project |
1
|
15
|
15
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
20
|
20
|
| Final Exam |
1
|
24
|
24
|
| Total |
150
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | To have adequate knowledge in Mathematics, Science and Industrial Engineering; to be able to use theoretical and applied information in these areas to model and solve Industrial Engineering problems. |
X | ||||
| 2 | To be able to identify, formulate and solve complex Industrial Engineering problems by using state-of-the-art methods, techniques and equipment; to be able to select and apply proper analysis and modeling methods for this purpose. |
X | ||||
| 3 | To be able to analyze a complex system, process, device or product, and to design with realistic limitations to meet the requirements using modern design techniques. |
X | ||||
| 4 | To be able to choose and use the required modern techniques and tools for Industrial Engineering applications; to be able to use information technologies efficiently. |
X | ||||
| 5 | To be able to design and do simulation and/or experiment, collect and analyze data and interpret the results for investigating Industrial Engineering problems and Industrial Engineering related research areas. |
X | ||||
| 6 | To be able to work efficiently in Industrial Engineering disciplinary and multidisciplinary teams; to be able to work individually. |
X | ||||
| 7 | To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively; to be able to give and receive clear and comprehensible instructions |
|||||
| 8 | To have knowledge about contemporary issues and the global and societal effects of Industrial Engineering practices on health, environment, and safety; to be aware of the legal consequences of Industrial Engineering solutions. |
X | ||||
| 9 | To be aware of professional and ethical responsibility; to have knowledge of the standards used in Industrial Engineering practice. |
X | ||||
| 10 | To have knowledge about business life practices such as project management, risk management, and change management; to be aware of entrepreneurship and innovation; to have knowledge about sustainable development. |
X | ||||
| 11 | To be able to collect data in the area of Industrial Engineering; to be able to communicate with colleagues in a foreign language. |
X | ||||
| 12 | To be able to speak a second foreign at a medium level of fluency efficiently. |
|||||
| 13 | To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Industrial Engineering. |
X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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