| 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 | ||||||
| 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;
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| 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. |
| Related Sustainable Development Goals | |
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| Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Managment Skills Courses | ||
| Transferable Skill Courses |
| Week | Subjects | Required Materials |
| 1 | Introduction: What is Data Science? Relationship of Data Science to Machine Learning | Chapter 1. Sections 1.1-1.3. Data Science from Scratch: First Principles with Python, J. Grus, ISBN9781491901427 |
| 2 | Getting data: reading files, scraping web, using APIs. Working with data: exploring data, basic data cleaning and munging | Chapter 9. Sections 9.1-9.5. Chapter 10. Sections 10.1-10.4. Data Science from Scratch: First Principles with Python, J. Grus, ISBN9781491901427 |
| 3 | Exploratory Data Analysis: visualizing data, plots, summary statistics, mean and dispersion | Chapter 3. Sections 2.1-1.4. Chapter 5. Sections 5.1. Data Science from Scratch: First Principles with Python, J. Grus, ISBN9781491901427 |
| 4 | Elements of probability: populations and samples, random variables, correlation, statistical dependence and independence, Bayes theorem | Chapter 6. Sections 6.1-6.5. Chapter 5. Sections 5.2-5.5. Data Science from Scratch: First Principles with Python, J. Grus, ISBN9781491901427 |
| 5 | Statistical inference: hypothesis and tests, statistical models, linear models, maximum likelihood inference, p-values, confidence intervals | Chapter 7. Sections 7.1-7.6. Chapter 14. Sections 14.1, 14.3. Data Science from Scratch: First Principles with Python, J. Grus, ISBN9781491901427 |
| 6 | Using Machine Learning methods for prediction – regression, multivariate linear regression, and k-Nearest Neighbors | Chapter 14. Sections 14.1-14.2. Chapter 15. Sections 15.1-15.5. Chapter 12. Sections 12.1-12.2. Data Science from Scratch: First Principles with Python, J. Grus, ISBN9781491901427 |
| 7 | Midterm exam | |
| 8 | Using Machine Learning for prediction – classification, logistic regression, linear discriminant classifier, largest margin classifier (SVM), and Naive Bayes | Chapter 16. Sections 16.1-16.5. Chapter 13. Sections 13.1-13.4. Data Science from Scratch: First Principles with Python, J. Grus, ISBN9781491901427 |
| 9 | Correctness when using Machine Learning: over-fitting, bias-variance tradeoff, cross-validation, feature selection | Chapter 11. Sections 11.4-11.6. Data Science from Scratch: First Principles with Python, J. Grus, ISBN9781491901427 |
| 10 | Feature Engineering: designing features, different types of features, relationship of features to models, relationship of data to features. Cleaning data: fixing data formats, fixing missing and damaged data, standardizing data (scaling and whitening) | Chapter 3. Sections 3.1-3.4. The Art of Data Science, R. D. Peng, E. Matsui; Chapter 4. Section 4.1-4.6. Python Machine Learning, S. Raschka, ISBN9781783555147 |
| 11 | Unsupervised data exploration – hierarchical clustering, k-means clustering | Chapter 19. Sections 19.1-19.6. Data Science from Scratch: First Principles with Python, J. Grus, ISBN9781491901427 |
| 12 | Unsupervised data exploration – association mining, dimensionality reduction | Chapter 10. Data Science from Scratch: First Principles with Python, J. Grus, ISBN9781491901427 |
| 13 | Decision Trees and Random Forests | Chapter 17. Sections 17.1-17.6. Data Science from Scratch: First Principles with Python, J. Grus, ISBN9781491901427 |
| 14 | Project presentations | |
| 15 | Project presentations | |
| 16 | General semester review |
| Course Notes/Textbooks | J. Grus, “Data Science from Scratch: First Principles with Python”, O’Reilly Media, 2015, ISBN9781491901427 ; 9781491904381 (Ebook) |
| Suggested Readings/Materials | 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 |
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury | ||
| Project | 1 | 25 |
| Seminar / Workshop | ||
| Oral Exam | ||
| Midterm | 1 | 25 |
| Final Exam | 1 | 50 |
| Total |
| Weighting of Semester Activities on the Final Grade | 2 | 50 |
| Weighting of End-of-Semester Activities on the Final Grade | 1 | 50 |
| Total |
| 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 | 14 | 2 | 28 |
| Field Work | |||
| Quizzes / Studio Critiques | |||
| Portfolio | |||
| Homework / Assignments | |||
| Presentation / Jury | |||
| Project | 1 | 30 | |
| Seminar / Workshop | |||
| Oral Exam | |||
| Midterms | 1 | 20 | |
| Final Exams | 1 | 24 | |
| Total | 150 |
| # | 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. | |||||
| 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. | |||||
| 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. | |||||
| 11 | To be able to collect data in the area of Industrial Engineering; to be able to communicate with colleagues in a foreign language. | |||||
| 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
