Дмитро

Nalyvayko Dmytro
Intern Data Scientist
PROFILE
Motivated student studying the fundamentals of Data Science
CONTACT with a strong desire to grow in the fields of data analysis, machine
[open contact info](look above in the "contact info" section) learning, and neural network development. Continuously
improving skills in programming, mathematics, and algorithms.
[open contact info](look above in the "contact info" section) Seeking opportunities to apply theoretical knowledge in practice
through internships, team projects, or independent research.
Lviv, Ukraine Possesses strong self-learning abilities, analytical thinking, and a
willingness to tackle challenging problems in this field.

EDUCATION
EXPERIENCE
Bachelor of Cybersecurity Programming Teacher
Lviv Polytechnic National University
IT STEP Academy | Lviv
2020-2021
Nov 2020 - present
Teaching courses in Python,C#,Unity for students of various ages.
Bachelor of Computer
Developing and managing student’s projects using the mentioned
Science technologies. Assisting students in solving technical issues and
IT STEP University supporting their learning process.
2021-present

Pet Projects
SKILLS Data Transformation and Visualisation
Python The acquisition of basic skills in data transformation, visualization,
and processing was achieved through working on tasks using the
PyTorch
Titanic dataset and the libraries NumPy, pandas, matplotlib, and
Numpy seaborn.

Matplotlib Classification
In this work, a comprehensive approach to processing the bank-
Pandas
additional-full dataset was carried out, which included: data
PySpark analysis, data preparation for further processing, visualization, and
transformation of these data. The analysis and modeling were
Git performed using the sklearn, seaborn, and matplotlib libraries.
Subsequently, a baseline classification model was developed.
Jupyter
Logistic regression
Docker
Based on the previous classification task using the same dataset,
repeated data analysis, transformation, and processing were
MODELS performed. Additionally, detailed visualization was conducted, and
XGBoost a logistic regression model was built using the bank-additional-
full dataset.
LightGBM
Izolated Forest Data Forecasting task

One class SVM Prediction of the hourly ambulance calls count based on the
environmental and seasonal settings.
Event and Anomaly Detection. Define the ambulance calls
LANGUAGES patterns with respect of special events and define the cases that
might be considered as abnormal behavior.
Ukrainian
Image classification.
English
Dog emotion classification using Pytorch and Torchvision. For
image classification task convolutional neural network trained
from skrech was used.