| 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 knowledge in Mathematics, science, physics knowledge based on mathematics; mathematics with multiple variables, differential equations, statistics, optimization and linear algebra; to be able to use theoretical and applied knowledge in complex engineering problems | |||||
| 2 | To be able to identify, define, formulate, and solve complex mechatronics engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose. | |||||
| 3 | To be able to design a complex electromechanical system, process, device or product with sensor, actuator, control, hardware, and software to meet specific requirements under realistic constraints and conditions; to be able to apply modern design methods for this purpose. | |||||
| 4 | To be able to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Mechatronics Engineering applications; to be able to use information technologies effectively. | |||||
| 5 | To be able to design, conduct experiments, collect data, analyze and interpret results for investigating Mechatronics Engineering problems. | |||||
| 6 | To be able to work effectively in Mechatronics Engineering disciplinary and multidisciplinary teams; to be able to work individually. | |||||
| 7 | To be able to communicate effectively in Turkish, both in oral and written forms; 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 global and social impact of engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of engineering solutions. | |||||
| 9 | To be aware of ethical behavior, professional and ethical responsibility; information on standards used in engineering applications. | |||||
| 10 | To have knowledge about industrial practices such as project management, risk management and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development. | |||||
| 11 | Using a foreign language, he collects information about Mechatronics Engineering and communicates with his colleagues. ("European Language Portfolio Global Scale", Level B1) | |||||
| 12 | To be able to use the second foreign language at intermediate level. | |||||
| 13 | To recognize the need for lifelong learning; to be able to access information; to be able to follow developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Mechatronics Engineering. | |||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
