Course Information

Course Information

Course Title	Code	Semester	L+U Hour	Credits	ECTS
INTRODUCTION TO DATA SCIENCE	PEC206	4. Semester	3 + 0	3.0	6.0

Prerequisites

None

Language of Instruction	English
Course Level	Bachelor's Degree
Course Type	Compulsory
Mode of delivery
Course Coordinator
Instructors	Emrah ER
Assistants
Goals	This course introduces the fundamentals of data science, which developed rapidly in the 21st century and was very popular with both researchers and practitioners. The course transfers the basic skills that a data scientist should have and enables the student to apply them to different fields such as medicine, defense industry, engineering, and finance. Objectives of the course 1) Identification of the problem, which are the necessary steps for the discovery of information in a given data, the collection, integration, management, analysis, and visualization of the data, 2) Understanding the importance of issues such as the size, variety, change, and value of the data and this How to approach types of differences, 3) Presenting basic statistical and machine learning approaches that can be used in the discovery of information in the data, 4) Extracting meaningful information from data with powerful alternative techniques such as network modeling and graph analysis, 5) How effective data presentation is for communication and 6) Understanding the basics of recommendation systems.
Course Content	This course includes: A general introduction to Data Science, probability, statistics and linear algebra. Basic data models, relational models of entities, relational models and SQL., SQL to NoSQL, non-relational databases and related data models XML Model and Xquery The NoSQL database, the state of the Mongo database. Sources and types of big data, frequently occurring pattern analysis. Students' presentations on research topics and techniques. Students' presentations on research topics and techniques. Midterm. Clustering, Classification, Incremental data analysis, and scalable methods for data management and analysis. Network models and graphical analysis. Visualization of data. Advice systems topics.
Learning Outcomes	1) Fundamentals of data science and understanding of the different abilities a data scientist should have. 2) Understanding the basics of data collection, modeling and management of data, which are among the processes of data science. 3) Understanding of the basic approaches for the presentation or display of data and the importance of the presentation of data for basic communication.