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Department of Computer Engineering
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
|
3
|
0
|
3
|
5
|
| Prerequisites | None | |||||
| Course Language | English | |||||
| Course Type | ELECTIVE_COURSE | |||||
| Course Level | First Cycle | |||||
| Mode of Delivery | Face-To-Face | |||||
| Teaching Methods and Techniques of the Course |
Group Work Problem Solving Lecture / Presentation |
|||||
| National Occupational Classification Code | - | |||||
| 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 |
X
|
|
| Supportive Courses |
|
|
| Media and Managment Skills Courses |
|
|
| Transferable Skill Courses |
|
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Required Materials | Learning Outcome |
| 1 | Introduction | Chapter 1 | LO1 |
| 2 | Input: Concepts, instances, attributes | Chapter 2 | LO1 |
| 3 | Output: Knowledge representation | Chapter 3 | LO1 |
| 4 | Data Visualization and Preprocessing | Chapter 7 | LO2 |
| 5 | Classification and Regression | Chapter 4 | LO4 |
| 6 | Time Series Analysis | Chapter 4 | LO3 |
| 7 | Association Mining | Chapter 4 | LO4 |
| 8 | Midterm Exam | - | |
| 9 | Clustering | Chapter 4 | LO4 |
| 10 | Evaluation | Chapter 5 | LO3 |
| 11 | Ensemble Learning | Chapter 6 | LO4 |
| 12 | Data Science Extensions and Applications | Chapter 8 | LO5 |
| 13 | Data Science Extensions and Applications | Chapter 8 | LO5 |
| 14 | Data Science Extensions and Applications | Chapter 8 | LO5 |
| 15 | Review of the Semester | - | |
| 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 | Weighting | LO1 | LO2 | LO3 | LO4 | LO5 |
| Project | 1 | 30 | X | X | X | X | X |
| Midterm | 1 | 30 | X | X | X | X | X |
| Final Exam | 1 | 40 | X | X | X | X | X |
| Total | 3 | 100 |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Participation | - | - | - |
| Theoretical Course Hours | 16 | 3 | 48 |
| Laboratory / Application Hours | - | - | - |
| Study Hours Out of Class | 14 | 2 | 28 |
| Field Work | - | - | - |
| Quizzes / Studio Critiques | - | - | - |
| Portfolio | - | - | - |
| Homework / Assignments | - | - | - |
| Presentation / Jury | - | - | - |
| Project | 1 | 24 | 24 |
| Seminar / Workshop | - | - | - |
| Oral Exams | - | - | - |
| Midterms | 1 | 25 | 25 |
| Final Exam | 1 | 25 | 25 |
| Total | 150 |
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
| # | PC Sub | Program Competencies/Outcomes | * Contribution Level | ||||
| 1 | 2 | 3 | 4 | 5 | |||
| No program competency data found. | |||||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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