COURSE SYLLABUS

DATA ANALYTICS

1	Course Title:	DATA ANALYTICS
2	Course Code:	END5505
3	Type of Course:	Optional
4	Level of Course:	Second Cycle
5	Year of Study:	1
6	Semester:	1
7	ECTS Credits Allocated:	7,5
8	Theoretical (hour/week):	3
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:	None
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Prof. Dr. TÜLİN İNKAYA
16	Course Lecturers:
17	Contactinformation of the Course Coordinator:	Prof. Dr. Tülin İnkaya E-posta: tinkaya@uludag.edu.tr Tel: +90 224 294 2605 Adres: Bursa Uludağ Üniversitesi, Endüstri Mühendisliği Bölümü, Görükle Bursa16059 Nilüfer / BURSA
18	Website:	ukey.uludag.edu.tr
19	Objective of the Course:	With the developing technology, large amount of data is stored in the production and service systems. Data science aims to contribute to the decision-making processes by analyzing these data and extracting meaningful and useful information. This course aims to introduce basic data science concepts, to provide the skills for application of the algorithms in this field to various databases, and to interpret the results.
20	Contribution of the Course to Professional Development	This course contributes to the professional development of the students by introducing basic concepts and information about data science, spanning the data science applications in business and science, and providing the ability to apply the knowledge they have learned.

Learning Outcomes:

1	Ability to comprehend basic data science concepts and data science methods.;
2	Ability to apply data mining algorithms to various data sets.;
3	Ability to evaluate and interpret the results obtained.;
4	Ability to follow current problems and research topics related to data mining.;

22	Course Content:

Week	Theoretical	Practical
1	Basic concepts about data science and data analytics
2	Data types, similarity and dissimilarity measures, and data visualization; applications in Weka
3	Data pre-processing and attribute selection
4	Classification - Decision trees and evaluation of classification result
5	Classification - Naive Bayes and k-nearest neighbor
6	Classification - Support vector machine and logistic regression
7	Classification - Neural networks and ensemble approaches; applications in Weka
8	Association rule mining
9	Clustering - k-means and its variations, hierarchical clustering
10	Clustering - Density based clustering, probability based approaches
11	Validation and evaluation of clustering result, applications in Weka
12	Outlier analysis
13	Data mining applications - Text mining, recommendation systems, spatio-temporal data mining
14	Project presentations

23	Textbooks, References and/or Other Materials:	G. Shmueli, N. R. Patel, P. C. Bruce, Data Mining for Business Intelligence: Concepts, Techniques and Applications in Microsoft Office Excel with XLMiner, 2nd Edition, John Wiley and Sons, 2010. P.-N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Pearson Addison Wesley, 2006.
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	0	0
Quiz	0	0
Homeworks, Performances	1	60
Final Exam	1	40
Total	2	100
Contribution of Term (Year) Learning Activities to Success Grade		60
Contribution of Final Exam to Success Grade		40
Total		100
Measurement and Evaluation Techniques Used in the Course	A three-stage project and a final exam
Information

ECTS / WORK LOAD TABLE

CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High