Position: Data Analyst
Category: Data Analyst
English level: intermediate
Years of experience: 0

The first work was performed in a JupiterNotebook software environment and using a Python programming language and the next by using a R programming language. A scientific article and a chapter in international monographwas published on the basis of the second work. It is also planned to publish the results of the third scientific work in the form of a separate publication.
I am very interested in the topic of time series, their analysis and forecasting.
Here are examples of educational projects where I applied my knowledge and skills:
1. The first term paper. In it, I selected data for the years 2016-2021 of the hryvnia currency market, analyzed them and built a neural network LSTM model to predict the closing price.
2. The second term paper, which also went to the international competition of student scientific papers Black Sea Science 2023. To solve the problem of modeling the relationship between the parameters of the public debt and macroeconomic indicators, a model of simulative equations(SEM) and a vector autoregressive model(VAR) were used. The econometric toolkit made it possible to compare the developed models in terms of forecasting and to describe the nature of interaction through the main factors affecting the size of the domestic public debt. Some statistical tests were also applied.
3. Scientific work, which became the winner of the international competition of student scientific works on the basis of Mykhailo Ostrogratsky Kremenchug National University. I used and compared between each other the diskriminant analysis model, logistic regression model and simple neural network model for task of classification of debt sequrity of Ukraine based on dynamics of macroeconomics indicators.
4. I`m also tried to single out precisely the monetary managed indicators and their separate interaction, which affects the size of the domestic public debt. I applied and compared the vector error correction model(VECM) and the autoregressive distribution-lag model(ARDL)