Data Science and Machine Learning
Faculty of Engineering

About Department

The Master of Science program in Data Science and Machine Learning aims to provide advanced theoretical and practical knowledge in machine learning, big data, and AI; equips students with skills to solve complex problems in multidisciplinary contexts; and prepare graduates for careers in tech industries, research, or further PhD studies.

Academic Staff

Course Plan

No. Code Name Semester Weekly Hours ECTS Year Type Syllabus
1 DS 501 Introduction to Data Science Fall 2 + 1 5.0 1 Elective
2 DS 502 Data Science in Python Fall 2 + 1 5.0 1 Elective
3 DS 507 Machine Learning and Deep Learning Fall 2 + 1 5.0 1 Elective
4 DS 508 Artificial Intelligence Fall 2 + 1 5.0 1 Elective
5 DS 509 Natural Language Processing Fall 2 + 1 5.0 1 Elective
6 DS 510 Pattern Recognition Fall 2 + 1 5.0 1 Elective
7 DS 511 Artificial Neural Networks Fall 2 + 1 5.0 1 Elective
8 DS 512 Web and Social Media Analytics Fall 2 + 1 5.0 1 Elective
9 IE 506 Advanced Project Management Fall 2 + 1 5.0 1 Elective
10 CE 502 Database Systems Fall 2 + 1 5.0 1 Elective
11 MATH 501 Probability Theory Fall 2 + 1 5.0 1 Elective
12 MATH 502 Random Processes Fall 2 + 1 5.0 1 Elective
13 MATH 503 Time Series Analysis Fall 2 + 1 5.0 1 Elective
14 MATH 504 Mathematics for Machine Learning Problems Fall 2 + 1 5.0 1 Elective
15 MATH 505 Principles of Game Theory Fall 2 + 1 5.0 1 Elective
16 MATH 506 Optimization Fall 2 + 1 5.0 1 Elective
17 EEE 501 Business Statistics Fall 2 + 1 5.0 1 Elective
18 EEE 502 Finance and Risk Analytics Fall 2 + 1 5.0 1 Elective
19 DS 503 Statistical Data Analysis Fall 2 + 1 5.0 1 Elective
20 DS 504 Machine Learning with Big Data Fall 2 + 1 5.0 1 Compulsory
21 DS 505 Advanced Data Mining Techniques Fall 2 + 1 5.0 1 Compulsory
22 DA 506 Bayesian statistics Fall 2 + 1 5.0 1 Compulsory
23 RМAW 590 Research methods and academic writing Fall 2 + 1 5.0 1 Compulsory
24 DS 599 Master thesis Spring 0 + 0 30.0 1 Compulsory
Course Name Semester Syllabus
First Year
Introduction to Data Science Fall
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Data Science in Python Fall
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Machine Learning and Deep Learning Fall
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Artificial Intelligence Fall
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Natural Language Processing Fall
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Pattern Recognition Fall
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Artificial Neural Networks Fall
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Web and Social Media Analytics Fall
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Advanced Project Management Fall
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Database Systems Fall
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Probability Theory Fall
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Random Processes Fall
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Time Series Analysis Fall
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Mathematics for Machine Learning Problems Fall
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Principles of Game Theory Fall
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Optimization Fall
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Business Statistics Fall
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Finance and Risk Analytics Fall
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Statistical Data Analysis Fall
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Machine Learning with Big Data Fall
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Advanced Data Mining Techniques Fall
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Bayesian statistics Fall
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Research methods and academic writing Fall
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Master thesis Spring
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