nhs-data-science-introduction

• 1.0 Python for Data Science
  • 1.1 Python Basics
  • 1.2 NumPy
  • 1.3 Pandas
  • 1.4 Matplotlib
  • 1.5 SciPy
  • 1.6 scikit-learn
• 2.0 Working with NHS Data
• 3.0 Data Science workflow
  • 3.1 Descriptive Statistics
  • 3.2 Data Visualisation
  • 3.3 Data Preparation
  • 3.4 Feature Selection
  • 3.5 Evaluate Model Performance
  • 3.6 Test-Train
  • 3.7 Cross Validation
  • 3.8 Algorithm Performance Metrics
    • 3.8.1 Classification Algorithms
    • 3.8.2 Regression Algorithms
  • 3.9 Hyper Parameter Tuning
  • 3.10 Automation
  • 3.11 Deployment
• 4.0 Ethics in Data Science
  • 4.1 Algorithmic Bias
  • 4.2 Privacy, Transparency and Trust
• 5.0 Reproducible analytical pipelines
  • 5.1 Coding in the open
    • 5.1.1 Open Data
    • 5.1.2 Privileged Credentials
    • 5.1.3 Sensitive Information
    • 5.1.4 Proprietary Information
  • 5.6 Quality Assurance (QA)
  • 5.7 Documentation
  • 5.8 Modular Code
  • 5.9 Unit Testing
  • 5.10 Tidy Data