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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
YBS 486BIG DATA AND MACHINE LEARNING3 + 05th Semester5

COURSE DESCRIPTION
Course Level Bachelor's Degree
Course Type Elective
Course Objective This course aims to teach basic algorithms for processing and analyzing big data and machine learning methods for data analysis problems students may encounter in their academic studies or careers.
Course Content Defining big data, discussing the uses of big data in social sciences and teaching the future of big data, Teaching how to process, clean and organize data in the form of big data, Practical teaching of methods used in analyzing big data, Introducing the basic algorithms of machine learning methods used in data analysis, Practical teaching of supervised and unsupervised machine learning algorithms, Teaching classification algorithms
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.

COURSE LEARNING OUTCOMES
1Understanding the definition and usage areas of big data leads to gaining knowledge that guides in big data analysis projects.
2Acquiring skills in processing, cleaning, and organizing large datasets enables effective work in real-world data analysis scenarios.
3Through the application of analysis methods and machine learning algorithms, their ability to solve data-driven problems improves.
4Through recognizing basic machine learning algorithms and learning classification algorithms, they can make more efficient and accurate decisions in data analysis processes.

COURSE'S CONTRIBUTION TO PROGRAM
Data not found.

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)13339
Assignments11010
Mid-terms11313
Final examination12626
Total Work Load

ECTS Credit of the Course






130

5
COURSE DETAILS
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L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes