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| BIL3020 | Introduction to Data Science | 3+0+0 | ECTS:4 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of COMPUTER ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Murat AYKUT | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The course intends to teach the students for the fundamentals of data science, data preprocessing operations, data reduction methods, learning approaches, and data visualization techniques with practical code examples. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Learns the basic concepts of data science. | 1 | 1, | | LO - 2 : | Gain knowledge on data preprocessing, data reduction, and data augmentation methods. | 1 | 1,3, | | LO - 3 : | Gain knowledge on learning from data approaches. | 1 | 1,3, | | LO - 4 : | Gain knowledge on evaluation approaches of learning methods. | 1 | 1, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| Introduction; Data Types; Data Preparation; Dealing with Missing Values; Dealing with Noisy Data; Data Reduction; Data Augmentation; Feature Selection; Instance Selection; Outlier Removal; Discretization; Supervised Learning; Regression Modeling; Unsupervised Learning; Model Evaluation; Association Rules; Data Summarization and Visualization. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction, Data Types | | | Week 2 | Data Preprocessing, Missing Value, Noisy Data | | | Week 3 | Data Reduction: Feature Selection, Feature Extraction | | | Week 4 | Data Reduction: Case Reduction, Feature Discretization | | | Week 5 | Data Augmentation | | | Week 6 | Outlier Removal | | | Week 7 | Supervised Learning: Logistic Regressiob, kNN, Decision Trees | | | Week 8 | Supervised Learning: Naive Bayes, SVM, Ensemble Learning | | | Week 9 | Midterm exam / Homework | | | Week 10 | Regression Modelling | | | Week 11 | Unsupervised Learning: k-Means, Expactation-Maximization, Hierarchical Clustering | | | Week 12 | Model Evaluation | | | Week 13 | Association Rules: Apriori, FP-Growthi Collaborative Filtering | | | Week 14 | Fundamentals of Text Mining | | | Week 15 | Data Summarization and Visualization | | | Week 16 | Final exam | | | |
| 1 | Larose, C. D., Larose, D. T. 2019; Data Science Using Python and R, Wiley Publishing, 256 pages.
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| 1 | Garcia, S., Luengo, J., Herrera, F. 2015; Data Preprocessing in Data Mining, Springer, 320 pages. | | | 2 | Igual, L., Seguí, S. 2017; Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications, Springer, 218 pages. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Homework/Assignment/Term-paper | 11 | 22/05/2023 | 2 | 50 | | End-of-term exam | 15 | 20/06/2023 | 2 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 3 | 14 | 42 | | Ödev | 8 | 1 | 8 | | Dönem sonu sınavı için hazırlık | 6 | 1 | 6 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 100 |
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