BIL3XXX Introduction to Data Science 3+0+0 4
Year / Semester Spring semester
Level of Course First Cycle
Status Elective
Department Computer Engineering
Prerequisites and co-requisites  N/A
Mode of Delivery Face to face
Contact hours 14 weeks
Lecturer Asst. Prof. Dr. Murat AYKUT
Co-Lecturer  
Language of instruction Turkish
Internship N/A
 
Objectives 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. 2, 4 1
LO - 2 : Gain knowledge on data preprocessing and data reduction methods. 2, 4 1,3
LO - 3 : Gain knowledge on learning from data approaches. 2, 4 1,3
LO - 4 : Gain knowledge on data visualization. 2, 4 1,3
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
Contents of the Course
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.
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  
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  
Textbook / Material
1 Chantal D Larose, Daniel T. Larose, "Data Science Using Python and R", Wiley, 2019, 256 pages.  
Recommended Reading
2 Salvador Garcia, Julian Luengo, Francisco Herrera, "Data Preprocessing in Data Mining", Springer, 2015, 320 pages.
3 Laura Igual, Santi Seguí, "Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications", Springer, 2017, 218 pages.
Method of Assessment
Type of work Week No  Date Duration (hours) Weight (%)
Mid-term exam 9   2 50
Quiz        
Homework        
Term Project        
Final exam 16   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
Lectures (Interactive) 3 14 42
Own (personal) studies outside class 3 14 42
Own study for mid-term exam 6 1 6
Mid-term exam 2,0 1 2
Quiz     0
Own study for final exam 6 1 6
End-of-term exam 2 1 2
Other 1     0
Total work load     100

All announcements will be made, and resources will be shared via course Piazza page (join as a student to class "BIL 3020").