Position: Data Engineer
Category: Data Engineer
English level: upper
Years of experience: 1.5

Completed Courses:
1) Data Science Fundamentals by DataRoot Labs (October 2021)
2) Learn Python 3 Course by Codecademy (February 2021)
3) Analyzing Data with Python by edX (December 2020)
4) Python Basics for Data Science by edX (November 2020)
5) SQL for Data Science by edX (November 2020)
6) Practical Git: for Absolute Beginners(October 2021)
At this time helping as volunteer coordinator for students on Data Science Fundamentals course by DataRoot Labs.
While studying on Data Science Fundamentals, got basic knowledge/experience in the following:

1) Math Fundamentals for Data Science: Linear Algebra, Statistics&Probability
2) Practising basic Python algorithms & data structures, OOP  and Python libraries(numPy, matplotlib, pandas, sklearn) in Jupyter Notebook.
3) Supervised & Unsupervised Learning that included: labs on regression, classification, dimensionality reduction PCA for image, etc implemented in Jupyter Notebook.
4) Implemented project in PyCharm using template with a ready made structure and instructions. Created an app with DB using Postgres, DB and app interactions were done with the help of SQLAlchemy.  Created API methods and routes for app, used Flask for testing and Docker to deploy it.
5) The last project has been also implemented in PyCharm and Jupyter Notebook given a full working code example using any Kaggle data according to the preference. 
As data was initially clean, features were only label encoded
Trained the model using DecisionTreeClassifier(), RandomForestClassifier(), AdaBoostClassifier(), GradientBoostingClassifier().