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#Data scientist

• This is a curated list of delightful resources for everything you need to develop Machine Learning solutions.
• Each item in this list will teach you at least one distinct and significant skill or piece of information.
• There are three content levels:
  1. 🐥 Essential reading for all ML engineers
  2. 🐍 Advanced reading for professional ML engineers
  3. 🦄 Expert material for expert ML engineers
• Descriptions are written to complete the sentence "After reading this article you will have learned ...".

• Communication
• Software Engineering
• Machine Learning
• DevOps

• 🐥 BLUF: The Military Standard That Can Make Your Writing More Powerful - How to make your communication more powerful (5 min)
• 🐥 The XY Problem - How to focus on explaining your end goal when asking for help (5 min)
• 🐥 Bike-shedding: how mature are you as an engineer? - How to avoid and call out bike-shedding (5 min)
• 🐥 E-mail like a boss - How to write better e-mails (5 min)
• 🐥 Stop Swiss Cheesing your calendar - How to manage your calendar so you can focus (15 min)
• 🐥 How to write in plain English - How to write in plain English (30 min)
• 🐥 Presentation Rules - How to create a great slide deck (30 min)
• 🐍 SMART criteria - How to define goals (15 min)
• 🐍 MECE principle - How to fully decompose a problem into a structured list (15 min)
• 🐍 SCQA: What is it, how does it work, and how can it help me? - How to structure your presentations, proposals, and sales outlines (15 min)
• 🐍 No More Misunderstandings - How to avoid miscommunication by paraphrasing (15 min)
• 🦄 Nonviolent communication - How to deliver constructive feedback in difficult situations (15 min)
• 🦄 The Halo effect - How to recognize and use the Halo effect to your advantage (15 min)
• 🦄 Mythical Man Month - The relationship between person-days and throughput time in a project (15 min)
• 🦄 Four-sides model - How to communicate effectively by considering how the receiver interprets your message (30 min)

• 🐥 Semantic Versioning - How to bump the version of your apps and packages (15 min)
• 🐥 __all__ and wild imports in Python - How __all__ defines the public API of your Python packages (15 min)
• 🐥 APIs for Machine Learning - How to design RESTful APIs for Machine Learning applications (30 min)
• 🐍 FastAPI docs - How to build RESTful APIs that correspond one-to-one with an OpenAPI specification (1 day)
• 🐍 The Rule of Three - When to build reusable components and when not (15 min)
• 🐍 Falsehoods programmers believe about time - How to avoid common pitfalls about time (15 min)
• 🐍 Falsehoods programmers believe about names - How to avoid common pitfalls about names (15 min)
• 🐍 Command Line Interface Guidelines - How to write great CLIs (1 hour)
• 🦄 Zalando's RESTful API guidelines - How to design RESTful APIs (1 day)

• 🐥 Poetry Cookiecutter - How to scaffold a modern Poetry-based development environment for Python packages and apps (30 min)
• 🐥 The seven rules of a great Git commit message - How to write great Git commit messages (15 min)
• 🐥 Learn Git Branching - Practice Git from beginner to advanced (1 hour)
• 🐍 Keep a Changelog - How to keep a changelog for your apps and packages (30 min)
• 🐍 Conventional Commits - How to prefix your commit messages to automate Semantic Versioning and Keep a Changelog (15 min)
• 🐍 Testing Python Applications with Pytest - How to properly test a package with pytest (30 min)
• 🐍 A successful Git branching model - How to release software with Git (15 min)
• 🐍 Code Review Best Practices - What to look for when reviewing a Pull Request (30 min)
• 🐍 Code Health: Respectful Reviews == Useful Reviews - How to communicate code review comments respectfully (15 min)
• 🐍 The Code Review Pyramid - What to look for and what to automate when reviewing a Pull Request (15 min)
• 🦄 Poetry workspace plugin - How to create and manage a Poetry-based monorepo (15 min)

• 🐥 PEP20 "The Zen of Python" - How to write idiomatic Python (15 min)
• 🐥 The Definitive Guide to Python import Statements - How to write import statements (30 min)
• 🐥 Understanding Python's logging module - How to use the logging module effectively (30 min)
• 🐍 Don't run code at import time - Why you shouldn't run code at import time
• 🐍 Please fix your decorators - Why you should probably use wrapt to write your decorators (30 min)
• 🦄 Do not log - What you should be doing instead of logging (30 min)
• 🦄 The Little Book of Python Anti-Patterns - A collectiong of Python anti-patterns (X hours)
• 🦄 Effective Python - A collection of Python idioms (X hours)
• 🦄 Python Design Patterns - A collection of software architecture patterns (1 hour)
• 🦄 SOLID - A standard set of software architecture patterns (1 hour)
• 🦄 What the f*ck Python! - How to master Python by understanding its edge cases (1 day)

• 🐍 The Comprehensive Guide to mypy - How to write type annotations in Python (1 hour)
• 🐍 Pydantic overview - How to write type annotations for complex types instead of a meaningless Dict[str, Any] (1 hour)
• 🐍 Magic number - Why magic values are an anti-pattern (15 min)
• 🐍 Enums - How to write Enums in Python instead of type-unsafe magic values (15 min)
• 🦄 Mypy generics - How to use TypeVars to write generic types such as List[T] (30 min)
• 🦄 Mypy protocols - How to use Protocols to define interfaces such as Iterable (30 min)

• 🐍 cookiecutter - Scaffold new Python packages or apps quickly with a Cookiecutter template
• 🐍 cruft - Update a Python package's underlying Cookiecutter scaffolding
• 🐍 commitizen - Check that commit messages satisfy Conventional Commits and automate Semantic Versioning and Keep a Changelog
• 🐍 poetry - Manage the packaging and dependencies of your Python project
• 🐍 poe - Define and run tasks in a Poetry project with Poe the Poet
• 🐍 poetry-workspace-plugin - Manage a Python monorepo with this Poetry plugin

• 🐥 black - Automatically format your code
• 🐥 isort - Automatically sort your import statements
• 🐍 pre-commit - Automatically run code quality checks on commit
• 🐍 bandit - Find common security issues
• 🐍 darglint - Check that your docstrings match your function signature
• 🐍 flake8 - Check your code for bugs and that your code style is PEP8-compliant
• 🐍 flake8 extensions - An awesome list of Flake8 extensions
• 🐍 mypy - Check the type-correctness of your code
• 🐍 pre-commit hooks - A collection of pre-commit hooks that check file quality
• 🐍 pydocstyle - Check that your code is documented
• 🐍 pygrep hooks - A collection of pre-commit hooks that check for common Python code smells
• 🐍 pytest-recording - Record and play back HTTP requests in your pytest tests
• 🐍 pyupgrade - Check that your code is written using the latest Python language features
• 🐍 safety - Check that your dependencies don't have any known security vulnerabilities
• 🐍 shellcheck - Check the quality of your shell scripts
• 🐍 coverage.py - Check your code's test coverage
• 🦄 hypothesis - Write tests that automatically look for edge cases that break your code
• 🦄 hypothesis-auto - Automate generate Hypothesis tests based on your code's type annotations

• 🐍 fastapi - Create RESTful APIs based on type annotations
• 🐍 typer - Create CLIs based on type annotations
• 🐍 streamlit - Create web apps with a single Python file

• 🐍 bump2version - Release a new version of your package
• 🐍 coloredlogs - Increase your logs' readability with colour
• 🐍 hvplot - Create interactive plots from pandas dataframes
• 🐍 mkdocs - Create developer documentation for your project
• 🐍 pdoc - Generate API documentation for your code
• 🐍 birdseye - Graphically debug your Python code
• 🐍 scalene - Profile your code's CPU and memory usage by line
• 🐍 viztracer - Vizualize your code's performance with a flamegraph
• 🐍 tqdm - Easily add progress bars to long-running jobs

• 🐍 Bias-variance tradeoff - How a model's total error is the sum of bias and variance (30 min)
• 🐍 The two different uses of cross-validation - How to use nested cross-validation to combine the two different uses of cross-validation (30 min)
• 🐍 Modes, Medians and Means: A Unifying Perspective - Why minimizing the Mean Absolute Error (MAE) is more robust than minimizing the Mean Squared Error (MSE) (30 min)
• 🐍 Backpropagation is the chain rule to compute the gradient - How backpropagation is an algorithm to compute the objective function's gradient (30 min)
• 🐍 Stacked generalization - How to stack models (30 min)
• 🐍 We have been using the wrong initialization for t-SNE and UMAP - How to initialize t-SNE and UMAP properly (15min)
• 🦄 From classic Fully Connected Networks to Transformers - How neural networks evolved from Fully Connected Networks to Transformers (30 min)
• 🦄 What is the .632+ rule? - How to measure generalization performance with bootstrapping (30 min)
• 🦄 Stacking strategies with and without leaks - Different strategies to stack models (30 min)
• 🦄 Data Distribution Shifts and Monitoring - How to detect and address the different types of data shift (1 hour)
• 🦄 Backprop is not just the chain rule - How backpropagation relates to Lagrange multipliers (30 min)
• 🦄 Why ML algorithms are hard to tune - Optimize multiple objectives when the Pareto front is concave (30min)
• 🦄 Deep learning model compression - How quantization, pruning, and distillation can be used to compress models (30 min)

• 🐍 SHAP: SHapley Additive exPlanations - How to explain a model's output with Shapley values (30 min)
• 🦄 Intro to Shapley and SHAP - How Shapley values are approximated by SHAP (30 min)

• 🐍 UMAP: Uniform Manifold Approximation and Projection - How to reduce dimensionality for visualization and modelling (30 min)
• 🐍 PyNNDescent - How to find nearest neighbours in huge datasets (15 min)

• 🐥 Precision and recall - How precision and recall measure a classifier's performance (30 min)
• 🐍 Probability calibration - How and for which model types you should calibrate the model's output scores into probabilities (30 min)
• 🐍 You're all calculating churn rates wrong - Correctly define what churn is (30 min)

• 🦄 Gaussian processes - From scratch - How to build probabilistic regression models with Gaussian Processes (1 hour)

• 🐍 Microsoft's Document Image Transformer - A self-supervised pre-trained model that achieves SotA performance on PubLayNet and can be used for various downstream tasks (30 min)

• 🐍 Awesome Sentence Embedding - A curated list of pretrained sentence and word embedding models (15 min)

• 🐍 The Prophet model - How Meta's Prophet model decomposes a time series into a trend, seasonality, and holiday components (30 min)
• 🐍 Darts - Time Series Made Easy in Python - How to build forecasting models with darts (1 hour)

• 🐍 Microsoft Recommenders - A comparison of recommender system models (30 min)

• 🐍 What I Wish Someone Had Told Me About Tensor Computation Libraries - How JAX, PyTorch, TensorFlow, and Theano are different (30 min)

• 🐍 Modern Pandas series (Part 1 - 7) - Write idiomatic pandas (1 hour)
• 🐍 Awesome Pandas - An awesome list of Pandas resources (1 hour)

• 🐥 Using scikit-learn Pipelines and FeatureUnions - How to use Pipelines to build end-to-end models (30 min)
• 🐥 Transforming target in regression - How to transform the target to build more robust models (15 min)
• 🐍 ColumnTransformer for heterogeneous data - How to use ColumnTransformer to process pandas DataFrames in sklearn Pipelines (30 min)
• 🐍 Custom Estimators - Create your own custom Estimator (30 min)
• 🐍 Hyperparameter optimization with successive halving - How to optimize hyperparameters with the most computationally efficient method (30 min)

• 🐍 Doccano - A tool for labelling text (30 min)
• 🐍 CVAT: Computer Vision Annotation Tool - A tool for labelling images (30 min)
• 🐍 Awesome Data Labelling - An awesome list of data labelling tools (30 min)

• 🐍 invoke - How to implement common tasks you run on your project as a CLI (30 min)
• 🐍 poe - How to implement common tasks you run on your project as a CLI (30 min)

• 🐥 Intro to packaging and dependency management for Python with Poetry - How to manage your Python package's dependencies and environment (30 min)
• 🐍 Intro to Pyenv for Machine Learning - How to use pyenv to manage your Python interpreter (30 min)
• 🐍 Modern Python Environments - dependency and workspace management - A comparison between pyenv, venv + pip, venv + pip-tools, poetry, pipenv, and conda (30 min)
• 🦄 Conda: Myths and Misconceptions - Common misconceptions about Conda (15 min)

• 🐥 Docker Curriculum - How to use Docker (4 hours)
• 🐍 Docker layer caching - How to write Dockerfiles to benefit from layer caching (30 min)
• 🐍 Dockerfile best practices - How to write good Dockerfiles (1 hour)
• 🐍 Configuring Gunicorn for Docker - How to best configure Gunicorn for a Docker image (30 min)
• 🐍 Speed up Docker with BuildKit’s new caching - How to speed up Docker builds with a build cache (30 min)
• 🦄 Build secrets in Docker and Compose, the secure way - How to use secrets in a Docker build (15 min)
• 🦄 Security scanners for Python and Docker - How to scan your Docker image for security issues with your code and Docker image (30 min)
• 🦄 The security scanner that cried wolf - How to scan your Docker image for security issues without false positives (15 min)
• 🦄 Awesome Docker - An awesome list of Docker resources (30 min)

• 🐍 Great Expectations - How to test and document your data and data pipelines (30 min)

• 🐍 Cron best practices - How to best use cron to schedule tasks (30 min)
• 🐍 A visual guide to SSH tunnels - How to forward ports and create tunnels with SSH (30 min)
• 🐍 Safe ways to do things in bash - How to write safe and robust shell scripts (1 hour)
• 🦄 Your terminal is not a terminal: An Introduction to Streams - How your terminal is a tool to manipulate streams (30 min)
• 🦄 Bash Heredoc - How to pass multiline arguments to commands with a heredoc (30 min)
• 🦄 Please stop writing shell scripts - Why you shouldn't write shell scripts for CI/CD or Docker images (30 min)

• 🐥 An Introduction to Terraform - How to use Terraform (1 hour)
• 🐍 Terraform best practices - Terraform best practices (1 hour)
• 🦄 Terraform pre-commit hooks collection - How to automate Terraform code quality checks with pre-commit (1 hour)
• 🦄 Awesome Terraform - An awesome list of Terraform resources (30 min)
• 🐥 Terraform Tutorial - How to get started with Terraform (1 hour)

• 🐥 Using Redis In-Memory Storage for your Python Applications - How to use Redis as an in-memory cache for your Python application (30 min)
• 🐍 Python Kafka Consumers: at-least-once, at-most-once, exactly-once - How to write different types of Kafka consumers in Python (30 min)
• 🦄 Kafka Exactly-Once-Semantics - How to produce and consume messages exactly once (1 hour)
• 🦄 RabbitMQ: a message queue library with persistance - RabbitMQ is a messaging system with a message broker (4 hours)
• 🦄 ZeroMQ: a socket library with message queue primitives - ZeroMQ is a lightweight messaging system without a message broker (8 hours)

(RADIX-AI)(https://github.com/radix-ai/awesome-machine-learning-engineer)