The aim of this course is to introduce the students the fundamental concepts in data science and show them how to apply data science approaches such as classification, regression, clustering, deep learning, forecasting, big data analysis, to real world scenarios.
Prerequisite(s)
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Corequisite(s)
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Special Requisite(s)
The minimum qualifications that are expected from the students who want to attend the course.(Examples: Foreign language level, attendance, known theoretical pre-qualifications, etc.)
Instructor(s)
Professor Ayça Çakmak Pehlivanlı
Course Assistant(s)
Schedule
This course is not offered in this semester.
Office Hour(s)
This course is not offered in this semester.
Teaching Methods and Techniques
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Course Schedules
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Assessments
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Program Outcomes
PO-1
Knowledge about management processes and management skills
PO-2
Knowledge and application skills related to the methods and competencies required for solving engineering problems
PO-3
Knowledge about developing areas of manufacturing and service sectors
PO-4
Ability to work in multi-disciplinary engineering teams
PO-5
Experience and knowledge of scientific research and publishing within the frame of academic ethics
Learning Outcomes
LO-1
combine different data from different data sources, transform the data into a single form and apply basic data cleaning techniques.
LO-2
understand what data means, how to use it and model the data on hand using classification, regression, clustering, deep learning, forecasting techniques and statistical models.
LO-3
apply visualization techniques on the data on hand and interpret the results.
LO-4
define the problems in real world scenarios using data.
LO-5
choose the most suitable machine learning techniques for solving real world problems.