Introduction to Data Science Technology

The discipline is aimed at developing the ability to synthesize and verify mathematical models, as well as to design specialized software for processing and analyzing data of various types and volumes.

The specificity of the course lies in the consideration, alongside classical Data Science methodologies, of advanced proprietary developments obtained during the implementation of practical R&D projects.

The theoretical foundations of Data Science are delivered through lectures with mandatory demonstrations of the studied algorithms in the form of program code examples. The structure of the theoretical course covers methodologies such as Statistical Analysis, Machine Learning, Artificial Intelligence, OLAP, Data Mining, and Text Mining for Decision Support Systems (DSS) and Expert Systems (ES).

Practical skills in applying Data Science technologies are acquired during laboratory sessions, with particular emphasis on software engineering processes. The practical part of the discipline focuses on the use of the high-level programming language Python, including libraries such as Pandas, SciPy, NumPy, Matplotlib, scikit-learn, TensorFlow, Keras, OpenCV, and PIL/Pillow.

The discipline reveals the essence of Data Science technological processes: data processing to obtain information – information processing to extract knowledge – practical application of skills – visualization of results.

The course is oriented toward the needs of positions such as Data Scientist, Data Engineer, and Data Analyst (Risk Team).

Lecturer:

Pysarchuk Oleksii Oleksandrovych