Position: Data Scientist
Category: Data Science
English level: upper
Years of experience: 0

Education
- A third-year student currently pursuing a CS degree at University of Lviv. 

Certifications
- Deep Learning course, from ODS
- Statistic Basics course on Stepik.
I am familiar with different aspects of machine learning field.

I have experience in working with both supervised and unsupervised learning algorithms, solving regression, classification and segmentation problems.

I know how to perform: EDA (exploratory data analysis) with pandas and numpy to detect outliers and missing values, different visualization using matplotlib and seaborn.
Knowing basics of statistics, i can perform different statistical significance tests for feature importance and engineering.

For building ML models, i have worked with scikit-learn, but also acknowledged about XGBoost and LightGBM libraries.
For DL task, i mostly use PyTorch. I know theory behind different neural net architectures including CNN's, RNN's, GAN's, LSTM mechanism and Attention.
Also, I am familiar with practical consequences of how this networks work and how to boost up training process using: advanced optimization algorithms (Momentum, Adam, etc...), Batch Normalization, LR annealing, Dropout, Cross Validation, Regularization and Transfer Learning.

On my GitHub and GitLab, you can find one of my ML projects, that covers various stages of ML pipeline.
Studying the problem of heart failure prediction, I've covered such topics as: data visualization (seaborn, matplotlib), statistical significance tests (parametric and nonparametric), overview and comparison of different classification metrics (f-beta score, ROC AUC, PR AUC), overview and comparison of different ML algorithms for classification (sklearn).
The model with the highest score is than deployed to Heroku. Users can interact with it using API built using FastAPI, via the simple web-site.
I expect to gain more knowledge and experience in Data Science and Python engineering.