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Course Code: 
CSE 464
Course Period: 
Spring
Course Type: 
Area Elective
Credits: 
3
Theoric: 
3
Practice: 
0
Laboratory Hour: 
0
ECTS: 
5
Course Language: 
English
Course Objectives: 
This course aims to provide an introduction to the data science and data analytics using the methods of statistical learning, an approach blending classical statistical methods with recent advances in computational and machine learning. The course will cover the main analytical methods from this field with hands-on applications using example datasets, so that students gain experience with and confidence in using the covered methods.
Course Content: 

Data Science and Big Data Analytics, Relational Databases and Data Modeling, Data Warehousing and Integration, Parallel Databases, Hadoop/ Mapreduce/Spark, Data Visualization, Machine Learning, Classification and Regression, Clustering, Natural Language Processing, Information Retrieval, Network Analysis

Course Methodology: 
1: Lecture, 2: Question-Answer, 3: Lab, 4: Case-study
Course Evaluation Methods: 
A: Testing, B: Experiment, C: Homework, D: Project

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