Position: Data Scientist
Category: Data Science
English level: upper
Years of experience: 0.5

Certificates from CISCO.
IT essentials – a course from CISCO to increase the level of IT Essentials, basic knowledge about the network, software, computer and their construction.
Networking essentials - a course from CISCO to increase the level of Networking Essentials, fundamental knowledge about the network communication, internet, connections, protocols.
Startup School. 
Course from Astana Hub about startups.
I do not have experiences yet, but I have projects that I did.
PROJECTS
Red Wine Quality – analysis, visualization, and machine learning
Using the red wine quality dataset, machine learning was done that determined which properties most affect the quality of wine (sklearn library), data visualization (matplotlib, seaborn libraries) and data cleaning (numpy, pandas libraries).
Top 10.000 popular movies – analysis, visualization
Using a dataset of 10,000 popular movies, visualizations were made (matplotlib, seaborn libraries) and data cleaning (numpy, pandas libraries) in order to reveal various kinds of information about films for the 20th, 21st centuries.


House price prediction – data preprocessing, visualization, feature engineering and machine learning(supervised)
Using dataset about the houses and their price in Bangalore, India, I did the progressive data preprocessing and visualization for further analysis by the help of the pandas, numpy and matplotlib libraries. The main purpose of this project is to predict the prices of the houses using machine learning, so I used 3 ML of supervised learning(regression): lasso, ridge and linear regression and I did grid search to use the best hyperparameters for ML using sklearn library.

Heart disease prediction – data preprocessing, visualization, feature engineering and machine learning(unsupervised)
Using dataset about habits and the medical characteristics of the person, I needed to predict does he have heart disease or no. As my previous project, I did the same things, but after data preprocessing, I choose the best technique to deal with imbalanced data using imblearn library. According to the purpose of the project, I used 3 ML of unsupervised learning(clustering) – decision tree, random forest and KNN and I did grid search to use the best hyperparameters for ML using sklearn library.