1. Pandas
    1. Pandas is the best library for data analysis! We can explore, clean, and analyze our data using different data sources
    2. Let's Learn
      1. Data Analysis with Python: Zero to Pandas | Jovian
      2. Learn Pandas Tutorials | Kaggle
      3. Descriptive statistics with python pandas
      4. Data Preprocessing with Python Pandas — Part 3 Normalisation | by Angelica Lo Duca | Nov, 2020 | Towards Data Science
      5. more Pandas
      6. Data Exploration with the dtale Library in Python
      7. 8 Python Pandas Value_counts() tricks that make your work more efficient
      8. The Best Exploratory Data Analysis with Pandas Profiling | by Matt Przybyla | Sep, 2020 | Towards Data Science
      9. A Quick Introduction to the “Pandas” Python Library | by Adi Bronshtein | Towards Data Science
      10. Package overview — pandas 1.1.1 documentation
      11. 75 Pandas Exercises with Solutions
      12. Data Cleaning
  2. NumPy
    1. NumPy allows us to work with N-dimensional arrays easy!
    2. Let's Learn
  3. Scikit-learn
    1. Scikit-learn is the great library for machine learning: predictive modeling and analysis.
      1. Some notes about decision trees
      2. allow to create different types of machine learning models
  4. Plotly
    1. Plotly is powerful tool for visualizations
      1. easy to use
      2. create dynamic dashboards
    2. Let's Learn
      1. The Next Level of Data Visualization in Python
      2. Interactive Visualizations with Plotly
      3. Шпаргалка по визуализации данных в Python с помощью Plotly
  5. Seaborn
    1. Seaborn is the most effective library for creating different visualizations to understand the models more properly
    2. One of the most important features of Seaborn is the creation of amplified data visuals. Some of the correlations that are not obvious initially can be displayed in a visual context, allowing Data Scientists to understand the models more properly.
    3. Due to its customizable themes and high-level interfaces, it provides well-designed and extraordinary data visualizations, hence making the plots very attractive, which can, later on, be shown to stakeholders.
    4. Let's learn
      1. Python Seaborn tutorial
      2. Data Visualization
  6. To Learn
    1. Gradio
      1. to build and deploy web apps for machine learning
    2. TensorFlow
      1. to implement neural networks
    3. Keras
      1. to create deep learning model
    4. SciPy
      1. to solve differential equations and much more
    5. Prophet
      1. Предсказываем будущее с помощью библиотеки Facebook Prophet
  7. More MindMaps
    1. Some notes about decision trees
    2. ML-продукты, разбираем основы
    3. my Jupiter notebooks
    4. and not
      1. https://www.kdnuggets.com/2021/03/top-10-python-libraries-2021.html
      2. The Ultimate Scikit-Learn Machine Learning Cheatsheet
      3. Data Science Learning Roadmap for 2021