Resources

TensorFlow notes (work-in-progress)

• Tensorflow setup options
• TensorFlow development environment GitHub repository: gperdrizet/tensorflow-GPU
• George’s TensorFlow 2.16 container on DockerHub: gperdrizet/tensorflow-gpu
• NVIDIA’s TensorFlow Release 24.06 container

How-to Guides & Instructions

1. DevOps Guides Overview
2. Plotting Overview
3. Statistics Overview
4. Data Wrangling Overview
5. Feature Engineering Overview
6. Regression Overview
7. Classification Overview
8. Ensemble Learning Overview
9. Unsupervised Learning Overview
10. Optimizers Summary

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Cheat sheets & syntax reference

1. Jupyter notebooks
2. VScode (Windows)
3. VScode (MacOs)
4. Git
5. NumPy
6. Pandas

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Data science library information

1. NumPy: A core library for efficient numerical computations and multi-dimensional array operations in Python.
   • Official documentation
   • GitHub repository
   • PyPI package page
2. Pandas: Provides high-level data structures (DataFrame, Series) and powerful tools for data manipulation and analysis.
   • Official documentation
   • GitHub repository
   • PyPI package page
3. Matplotlib: A versatile plotting library for creating static, animated, and interactive visualizations in Python.
   • Official documentation
   • GitHub repository
   • PyPI package page
4. Seaborn: A statistical data visualization library built on Matplotlib that provides attractive themes and higher-level plotting functions.
   • Official documentation
   • GitHub repository
   • PyPI package page
5. SciPy: A collection of scientific computing tools built on NumPy for optimization, integration, signal processing, and more.
   • Official documentation
   • GitHub repository
   • PyPI package page
6. Statsmodels: Offers classes and functions for estimating statistical models, conducting hypothesis tests, and performing data exploration.
   • Official documentation
   • GitHub repository
   • PyPI package

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Other interesting links

1. Further topics in data wrangling/data analysis
   • For an interesting alternative to Pandas see Polars
   • For N dimensional, labeled arrays see Xarray
   • For parallel, distributed dataframes see PySpark and Dask
   • For GPU accelerated data analysis see: CuPy and RAPIDS
   • For data pipeline workflow management see: Luigi or Airflow
2. Data visualization
   • The Python graph gallery: lots different plot examples
   • Data to vis: example plots by data type
   • PyWaffle ‘waffle’ style proportion plots in python
   • squarify square treemap layouts in Python

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Incremental capstone slides

Unit 2: Applied Data Science with Python

1. Incremental capstone 1: import and clean data

Unit 3: Machine Learning

1. Incremental capstone 5: Florida Bike Rentals