Computer Engineering

Course Code	Course Name	Year	Period	Semester	T+A+L	Credit	ECTS
05051108	Data Analytics	2025	Autumn	5	3+0+0	3	5

Course Type	Elective
Course Cycle	Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6)
Course Language	Turkish
Methods and Techniques	-
Mode of Delivery	Face to Face
Prerequisites	-
Coordinator	-
Instructor(s)	Asst. Prof. Neşe ÖZKAN YILMAZ
Instructor Assistant(s)	-

Basic Vocational Courses
Specialization / Field Courses
Support Courses	X
Transferable Skills Courses
Humanities, Communication and Management Skills Courses

Relationship Levels
Lowest	Low	Medium	High	Highest
1	2	3	4	5

#	Program Learning Outcomes	Level
P6	Ability to work effectively in disciplinary and multi-disciplinary teams; individual study skills	1
P9	To act in accordance with ethical principles, professional and ethical responsibility; Information on the standards used in engineering applications	2
P10	Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development	3

Upon the successful completion of this course, students will be able to:
No	Learning Outcomes	Outcome Relationship	Measurement Method **
O1	Learning the methods of training a simple ANN.	P.3.27	7
O2	Must have technological knowledge of basic measurement theory, sensors and other measurement components	P.4.1	1
O3	Ability to work independently and take responsibility	P.6.1	3
** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7

Week	Topics
1	Fundamental concepts related to data science and data analytics.
2	Data types, similarity and distance metrics, and data visualization; applications with Weka
3	Data preprocessing and feature selection
4	Classification – Decision trees and evaluation of classification results
5	Classification – Bayesian classification and k-nearest neighbors
6	Classification – Support vector engines and logistic regression
7	Classification – Artificial neural networks and ensemble methods, applications with Weka
8	Association analysis – Rule derivation
9	Clustering – k-means and their variations, hierarchical clustering
10	Clustering – Density-based clustering, probability-based approaches
11	Verification and evaluation of clustering results; applications with Weka
12	Outlier data analysis
13	Data mining applications – Text mining, recommendation systems, spatio-temporal data mining
14	Project presentations

Resources

G. Shmueli, N. R. Patel, P. C. Bruce, Data Mining for Business Intelligence: Concepts, Techniques and Applications in Microsoft Office Excel with XLMiner, 2. Basım, John Wiley and Sons, 2010.

In-Term Studies	Quantity	Percentage
Attendance	-	-
Laboratory	-	-
Practice	-	-
Course Specific Internship (If Any)	-	-
Homework	-	-
Presentation	-	-
Projects	-	-
Quiz	-	-
Midterms	1	40 (%)
Final Exam	1	60 (%)
Total		100 (%)

	Quantity	Duration	Total Work Load
Course Week Number and Time	14	3	42
Out-of-Class Study Time (Pre-study, Library, Reinforcement)	14	3	42
Midterms	1	32	32
Quiz	0	0	0
Homework	0	0	0
Practice	0	0	0
Laboratory	0	0	0
Project	0	0	0
Workshop	0	0	0
Presentation/Seminar Preparation	0	0	0
Fieldwork	0	0	0
Final Exam	1	34	34
Other	0	0	0
Total Work Load:			150
Total Work Load / 30			5
Course ECTS Credits:			5

Relationship Levels
Lowest	Low	Medium	High	Highest
1	2	3	4	5

#	Learning Outcomes	P3	P4	P6
O1	Learning the methods of training a simple ANN.	2	-	-
O2	Must have technological knowledge of basic measurement theory, sensors and other measurement components	-	3	-
O3	Ability to work independently and take responsibility	-	-	3