Insights By Scott Ernst

Project Autodidact

Project Details: https://insightsbyse.com/projectautodidact/

Scott Ernst Bio: https://insightsbyse.com/aboutscotternst/

Project Contact: InsightsBySE@protonmail.com

Progress Report Scope (S02-C02-M04)

Stage 2: Programming, Data Science, and Machine Learning Fundamentals and Applications

Cluster 2: Python Programming Fundamentals

Module 4: Python Runtime Analysis Tools

Summary Of Goals Achieved

1. Reviewed similarities and differences among Python runtime analysis tools, including scope, purpose, and key components: (1) data validation, (2) interactive debugging, (3) testing framework, (4) code coverage, (5) performance profiling, (6) memory profiling, (7) tracing and logging, and (8) model monitoring
2. Learned practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup (pre-commit hooks): Pydantic Mypy Plugin (data validation with pre-commit hooks)
3. Learned practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup (pre-commit hooks): ipdb (interactive debugging with pre-commit hooks)
4. Learned practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup (pre-commit hooks): Pytest (testing framework with pre-commit hooks)
5. Learned practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup (pre-commit hooks): Coverage.py (code coverage with pre-commit hooks)
6. Learned practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup: Pyinstrument (performance profiling without pre-commit hooks)
7. Learned practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup: Memory Profiler (memory profiling without pre-commit hooks)
8. Learned practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup: Loguru (tracing and logging without pre-commit hooks)
9. Learned practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup: Evidently AI (model monitoring without pre-commit hooks)

Part 1

Goal 1 Statement: Review similarities and differences among Python runtime analysis tools, including scope, purpose, and key components: (1) data validation, (2) interactive debugging, (3) testing framework, (4) code coverage, (5) performance profiling, (6) memory profiling, (7) tracing and logging, and (8) model monitoring

Goal 1 Plan: Read source materials

Goal 1 Work Product: List of best practices

Goal 1 Result: Completed

Part 2

Goal 1 Statement: Learn practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup (pre-commit hooks): Pydantic Mypy Plugin (data validation with pre-commit hooks)

Goal 1 Plan: Read source materials and complete practice problems

Goal 1 Work Product: Completed practice problems and list of best practices

Goal 1 Result: Completed

Part 3

Goal 1 Statement: Learn practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup (pre-commit hooks): ipdb (interactive debugging with pre-commit hooks)

Goal 1 Plan: Read source materials and complete practice problems

Goal 1 Work Product: Completed practice problems and list of best practices

Goal 1 Result: Completed

Part 4

Goal 1 Statement: Learn practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup (pre-commit hooks): Pytest (testing framework with pre-commit hooks)

Goal 1 Plan: Read source materials and complete practice problems

Goal 1 Work Product: Completed practice problems and list of best practices

Goal 1 Result: Completed

Part 5

Goal 1 Statement: Learn practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup (pre-commit hooks): Coverage.py (code coverage with pre-commit hooks)

Goal 1 Plan: Read source materials and complete practice problems

Goal 1 Work Product: Completed practice problems and list of best practices

Goal 1 Result: Completed

Part 6

Goal 1 Statement: Learn practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup: Pyinstrument (performance profiling without pre-commit hooks)

Goal 1 Plan: Read source materials and complete practice problems

Goal 1 Work Product: Completed practice problems and list of best practices

Goal 1 Result: Completed

Part 7

Goal 1 Statement: Learn practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup: Memory Profiler (memory profiling without pre-commit hooks)

Goal 1 Plan: Read source materials and complete practice problems

Goal 1 Work Product: Completed practice problems and list of best practices

Goal 1 Result: Completed

Part 8

Goal 1 Statement: Learn practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup: Loguru (tracing and logging without pre-commit hooks)

Goal 1 Plan: Read source materials and complete practice problems

Goal 1 Work Product: Completed practice problems and list of best practices

Goal 1 Result: Completed

Part 9

Goal 1 Statement: Learn practical utilization of Python runtime analysis tools for predictive or forecasting tasks or projects using Google Colab and GitHub, including Google Colab Setup and GitHub Setup: Evidently AI (model monitoring without pre-commit hooks)

Goal 1 Plan: Read source materials and complete practice problems

Goal 1 Work Product: Completed practice problems and list of best practices

Goal 1 Result: Completed