the-open-data-science-bootcamp

Introduction:

This work is an outgrowth of an intensive self-study program in data science and machine learning. The goal is to maintain a collection of tools, illustrative coding samples, and learning resources to augment a formal or self-directed data science 'bootcamp experience.' Enjoy!

** Note: Efforts have been made to include as much open-source content as possible. Feel free to suggest alternatives to paid content, where comparable alternatives exist.

Sample Schedule:

Contents:

• Software Engineering:

  • General:

    • Object Oriented Design Principles
    • Software Engineering Design Patterns
    • Exception Handling
    • Regular Expressions
    • Else

  • Language (Python)

    • Style Guide
    • Documentation
    • Linting
    • Logging
    • Documenting Projects
    • Testing
    • Language Deep Dive (Idioms, and more)
    • Data Science Specific
    • Else
      • Packaging Projects

  • Language (JavaScript)

    • Style Guide
    • Documentation
    • Linting
    • Logging
    • Documenting Projects
    • Testing
    • Language Deep Dive (Idioms, and more)
    • Else

• Web (*Data Science 'Core Web Topics.' Other topics included as boilerplate fullstack content.)

  • Tools
    • Server-Side Tools/Tech
      • Flask*
      • NodeJS, Express, Mongoose
    • Client-Side Tools/Tech
      • HTML*, CSS*, Sass, JSS
      • ReactJS
      • Bootstrap, Material Design
    • UI/UX
      • Figma
  • In Practice
    • Scraping
      • Beautiful Soup
    • Apis

• Algorithms

  • Data Structures
  • Searching and Sorting
  • Heaps
  • Trees
  • Graphs
  • Else

• Data

  • Databases and Libraries

    • ORMs
      • sqlalchemy
      • flask-sqlalchemy

  • Data Analysis

    • pandas

  • Data Mining

    • Scraping

  • Data Visualization

    • matplotlib
    • bokeh
    • altair
    • plotly
    • D3js

  • Else

  • Scientific Computing

    • numpy
    • sciPy

• Machine Learning

  • General:
    • scikit-learn
  • Deep Learning:
    • keras
    • tensorflow
    • pytorch

• Statistics

• DevOps

  • CI/CD
    • Circle CI
    • Jenkins
  • Containers
    • Docker
  • Container Orchestration
    • Docker Swarm
    • Kubernetes
  • Service Providers and Cloud Services
    • AWS
    • Google Cloud
  • Else
  • More Resources:
  • Docker Mastery: https://www.udemy.com/course/docker-mastery/
  • Devops: https://www.udemy.com/course/devops-training/

• Data and Computing at Scale:

  • Spark / PySpark
  • Kafka
  • Apache Storm
  • MapReduce
  • Else
    • hdf5, ...

Subject Courses, Lectures and Live Content:

• Statistics
  • Courses
    • Coming soon!
  • Texts:
    • All of Statistics - Larry Wasserman
      • https://www.amazon.com/All-Statistics-Statistical-Inference-Springer/dp/1441923225
• Machine Learning

  • ML Infrastructure and Pragmatics - Fast.ai (A popular high level api for PyTorch (will we see it in Swift?), with top-notch pragmatic courses in deep learning). - https://www.fast.ai/

    • Udemy:
      • Feature Selection:
        • https://www.udemy.com/course/feature-selection-for-machine-learning/
      • Feature Engineering:
        • https://www.udemy.com/course/feature-engineering-for-machine-learning/
      • Imbalanced Data:
        • https://www.udemy.com/course/machine-learning-with-imbalanced-data/
      • Deployment of Machine Learning Models (ml-infra):
        • https://www.udemy.com/course/deployment-of-machine-learning-models/
      • Testing and Monitoring Machine Learning Model Deployments (ml-infra):
        • https://www.udemy.com/course/testing-and-monitoring-machine-learning-model-deployments/
    • Books:
      • Building Machine Learning Powered Applications: Going from Idea to Product - Emmanuel Ameisen (Head of AI at Insight Data Science)
      • https://www.amazon.com/gp/product/149204511X/ref=ox_sc_act_title_1?smid=A2CI8D50F555BA&psc=1
      • Machine Learning Yearning (ML Pragmatics with Andrew Ng):
        • https://www.deeplearning.ai/machine-learning-yearning/

  • ML Coursework

    • Fundamentals
      • CS229 (Andrew Ng): Similar content to the Coursera Machine Learning with Andrew Ng
        • Course Website: http://cs229.stanford.edu/
        • Video Lectures: https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU
      • CS230 (Andrew Ng): Similar content to the Coursera deeplearning.ai specialization with Andrew Ng
        • Course Website: https://cs230.stanford.edu/
        • Video Lectures: https://www.youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb
    • Deeplearning
      • Machine Learning with Andrew Ng (The course that 'started a MOOC revolution'):
        • https://www.coursera.org/learn/machine-learning
      • DeepLearning.ai Specialization with Andrew Ng:
        • https://www.coursera.org/specializations/deep-learning
      • CS231n: Deep Learning for Computer Vision
        • Course Website: http://cs231n.stanford.edu/
        • Video Lectures: https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv
      • CS224n: Natural Language Processing with Deep Learning
        • Course Website: https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1194/
        • Video Lectures: https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z
    • Probabalistic Graphical Models
      • Coming soon!

  • Texts

    • The Deep Learning Textbook (Ian Goodfellow, Yoshua Bengio, and Aaron Courville):
      • https://www.deeplearningbook.org/

  • A Selection of Useful Blogs

  • A (Not so Principled Selection of) Noteworthy ML Researchers:

  • Conferences (There are many);

  • A (non-principled) Selection of Significant Papers:

    • LeNet
    • AlexNet
    • Attention is all you need

  • Model Implementations

    • Model Zoo

  • Data Sources

    • Computer Vision
    • Natural Language Processing
    • Reinforcement Learning

Illustrative Projects:

• Production Demo Pipelines: Scikit-Learn, Keras, Pytorch
• Twitter Sentiment
• Wiki
• Atari Deep Reinforcement Learning
• Business Case Study
• Papers to Code
• Reference Applications and Implementations

Else - Data Science in Practice ...

• Case Studies
• Interviews
• Business Analytics:
  • Metrics
  • A/B Testing

Resource Type Key:

* Cheatsheets:
* Documentation:
* Live Content (forumns, blogs, news, etc.)
* Video Tutorials:
* Written Tutorials:
* Video Lectures:
* Notebooks:
* Code Examples:
* Illustrative Projects:

Dependencies:

• The wiki and supporting markdown documents require MathJax to display mathematical content appropriately. You can add the extension for Google Chrome here: https://chrome.google.com/webstore/detail/mathjax-3-plugin-for-gith/peoghobgdhejhcmgoppjpjcidngdfkod/related?hl=en