PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
EKOM 537	BIG DATA ANALYSIS	3 + 0	1st Semester	7,5

COURSE DESCRIPTION

Course Level	Master's Degree
Course Type	Elective
Course Objective	To introduce basic concepts in predictive modelling, to introduce supervised and unsupervised learning, to introduce python programming language for machine learning, to introduce performance evaluation of predictive models
Course Content	Supervised and Unsupervised Learning, Linear regression, logistic regression, Naïve Bayes, K-nearest neighborhood, K-means, Hierarchical clustering, Artificial Neural Networks, Support Vector Machines, Big data examples for regression problems, Big data examples for classification problems, Big data examples for clustering problems, Performance Analysis
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Ability understand the basic concepts in predictive modeling
2	Ability to apply supervised models for classification and regression problems
3	Ability to apply unsupervised model for clustering problems
4	Ability to analyze and evaluate performance of the predictive methods
5	Ability to code basic machine learning techniques

COURSE'S CONTRIBUTION TO PROGRAM

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	5	70
Hours for off-the-classroom study (Pre-study, practice)	14	5	70
Assignments	1	10	10
Mid-terms	1	20	20
Final examination	1	25	25
Total Work Load ECTS Credit of the Course			195 7,5

COURSE DETAILS

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L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student