Akademik Dersler - Tanıtım


Code - Course Title	BİM465 - Introduction to Data Science
Course Type	Area Elective Courses
Language of Instruction	İngilizce
Laboratory + Practice	3+0
ECTS	4.5
Course Instructor(s)	ARAŞTIRMA GÖREVLİSİ DOKTOR GÖKHAN GÖKSEL
Mode of Delivery	This course is normally delivered face to face. However, in special circumstances such as pandemics and natural disasters, it is carried out in synchronous and/or asynchronous distance education format.
Prerequisites	There is no prerequisite or co-requisite for this course.
Courses Recomended
Recommended Reading List
Assessment methods and criteria	Quiz, Midterm Exam, Final Exam.
Work Placement
Sustainability Development Goals

Weeks	Topics
Week - 1	An Overview of Business Intelligence, Analytics, Data Science and Artificial Intelligence
Week - 2	Fundamentals of Data, Big Data and Statistical Modeling
Week - 3	Data Warehousing and Online Analytical Processing
Week - 4	Data Warehousing and Visualization
Week - 5	Pattern Mining: Basic Concepts and Methods in Feature Engineering
Week - 6	Classification
Week - 7	Midterm
Week - 8	Cluster Analysis
Week - 9	Deep Learning and Cognitive Computing
Week - 10	Deep Learning and Cognitive Computing
Week - 11	Outlier Detection
Week - 12	Business Analytics Tools
Week - 13	AI-Based Trends in Data Science
Week - 14	AI-Based Trends in Data Science

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
1.Midterm Exam	1	30
Quiz	1	30
Final Exam	1	40
Toplam (%)		100