Diana Kapatsyn
About Diana Kapatsyn
Diana Kapatsyn is a Junior Data Scientist specializing in natural language processing and deep learning models using PyTorch. She is pursuing a Master's degree in Data Science at Ukrainian Catholic University and has been working at Simporter since 2021.
Work at Simporter
Diana Kapatsyn has been employed at Simporter as a Junior Data Scientist specializing in Natural Language Processing (NLP) since 2021. In this role, she focuses on implementing and maintaining deep learning models using PyTorch. Her responsibilities include engaging in exploratory data analysis and hypothesis testing, utilizing modern statistical methods to derive insights from data. She also emphasizes data preparation techniques tailored for NLP tasks, contributing to the development and optimization of data-driven solutions.
Education and Expertise
Diana Kapatsyn is currently pursuing a Master's degree in Data Science at Ukrainian Catholic University, a program she has been enrolled in since 2022. Prior to this, she completed her Bachelor's degree in Computer Science at the National Technical University of Ukraine 'Kyiv Polytechnic Institute' from 2018 to 2022. Her educational background provides her with a solid foundation in data science principles and techniques, particularly in the context of NLP and deep learning.
Background
Diana Kapatsyn's academic journey includes significant study in the fields of Computer Science and Data Science. She obtained her Bachelor's degree in Computer Science from the National Technical University of Ukraine 'Kyiv Polytechnic Institute' in 2022. Following this, she began her Master's degree studies at Ukrainian Catholic University, focusing on Data Science. This educational path has equipped her with the necessary skills to excel in her current role as a Junior Data Scientist.
Technical Skills
Diana Kapatsyn specializes in implementing and maintaining deep learning models, particularly using the PyTorch framework. Her technical expertise includes exploratory data analysis, hypothesis testing, and modern statistical methods. She is skilled in data preparation techniques that are essential for natural language processing tasks, ensuring that data is well-structured and ready for analysis.