Data Science technologies for e-commerce tasks

The discipline is aimed at providing a theoretical foundation and practical skills for implementing Data Science stages in the field of e-commerce. For this purpose, students study the processes of synthesis and verification of mathematical models, as well as the development of specialized CRM and ERP systems 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 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 course is oriented toward the needs of positions such as Data Scientist, Data Engineer, and Data Analyst (Risk Team).

The acquired Data Science competencies can be applied in projects within the following application domains: data analysis for e-commerce tasks; data analysis for industrial and infrastructure CRM and ERP systems; analysis of visual and geospatial data across various domains.

Lecturer:

Pysarchuk Oleksii Oleksandrovych