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Data Science is my inspiration
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Data Science is my inspiration
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YaroslavaVob/README.md

I am Yaroslava 👋

👩ABOUT ME

I am an aspiring Data Scientist who has gained extensive experience in data analysis and machine learning.

Despite starting my journey in Data Science in June 2023, I have mastered Python and SQL.
Now I successfully apply modern tools for data mining, analysis, and building algorithms across a wide range of machine learning tasks.

${\color{red}My}{\color{red}lovely}{\color{red}environment}$

Jupyter Notebook Visual Studio Code

${\color{red}Competences}$

${\color{blue}1.Data}{\color{blue}Engineering,}{\color{blue}Data}{\color{blue}Mining}$

  • Base skills in relational and non-relational databases (key-value, column-based, document-based)
  • Web Scraping & Parsing: Proficient in tools like requests, Beautiful Soup (bs4), REST APIs, and Selenium
  • Big Data: Basic experience with Spark and Hadoop ecosystems

${\color{blue}2. Feature}{\color{blue}Engineering}$

  • Data Preparation: Cleaning and transforming datasets
  • Feature Creation: Generating new features to enhance model quality

${\color{blue}3. Exploratory}{\color{blue}Data}{\color{blue}Analysis}$

  • Analysis: Conducting mathematical and statistical analyses to uncover insights and dependences
  • Tools:
    • matplotlib, seaborn, plotly
    • numpy, pandas
    • scipy, statsmodels

${\color{blue}4.Machine}{\color{blue}Learning}$

  • Algorithm Development: Skilled in building models ranging from simple algorithms to ensemble models:
    • Linear/Logistic Regression, DecisionTree
    • Bagging, Boosting, Stacking
    • Clustering
    • Time Series
    • NLP
    • Recommendation System
    • Neural Network
  • Frameworks and libraries:
    • scikit-sklearn
    • catboost, xgboost
    • arima/garch, prophet
    • nltk, gensim
    • lightfm
    • lightAutoML
    • tensorflow
  • Optimization: Expertise in algorithm optimization with hyperparameter tuning tools like Hyperopt and Optuna.

${\color{blue}5. Evaluation}$

  • Metrics Knowledge: Comprehensive understanding of machine learning evaluation metrics and business KPIs to validate model performance.

${\color{blue}6. Deployment}$

  • Production Integration: Experience in deploying machine learning models using:
    • streamlit
    • flask
    • docker compose
    • rabbitMQ

$${\color{red}Favorite}$$ $${\color{red}tools}$$

postgreesql external-no-sql-data-science-outline-outline-black-m-oki-orlando redis--v1 mongo-db web-scraper Static Badge api-settings selenium-test-automation external-data-scraping-data-engineering-solid-solidglyph-m-oki-orlando statistics--v3 numpy pandas external-diagram-marketing-kmg-design-flat-kmg-design Matplotlib Plotly Static Badge SciPy scikit-learn Static Badge Static Badge Static Badge Static Badge Static Badge Static Badge Static Badge TensorFlow external-natural-artificial-intelligence-outline-black-m-oki-orlando docker flask hadoop-distributed-file-system artificial-intelligence streamlit Static Badge

📚 Training projects 📚

Plan for the near future 😍

  • Interesting remote job in a friendly ambitious team
  • Enhance knowledge and professional skills
  • Learning new tools for ML, deployment
  • Diligent learning of deep neural networks
  • Travelling

    • 💬 Connect to me on:

    Pinned Loading

    1. DataScience DataScience Public

      my student's projects

      Jupyter Notebook 3

    2. Linear-Algebra Linear-Algebra Public

      Jupyter Notebook 1

    3. Time-Series_project Time-Series_project Public

      Jupyter Notebook 1

    4. Library-managment-system Library-managment-system Public

      Jupyter Notebook