Data Science Guides

A repository containing the Data Science Guides, a book of worked examples in R & Python of some common data science tasks, to use as a space for learning how to implement these methods, and as a template for applying data science.

To access the R/Python versions of the book:

• R
• Python

Book Contents

• Welcome

• Introduction

• Doing Good Science

• Dealing with Data

  • Importing Data from SQL
  • Wrangling Data
  • Exploratory Data Analysis

• Statistical Inference

  • Hypothesis Testing
  • Linear Regression
  • Non-Linear Regression
  • Multilevel Regression

• Machine Learning

  • Designing Machine Learning Solutions
  • End-to-End ML Workflow
  • Validation & Evaluation

• Deep Learning

  • Computer Vision
  • Natural Language Processing
  • Reinforcement Learning

• Delivering Data Science

  • Automation
  • Orchestration
  • Communication

Acknowledgements

Several datasets used in this repository have been sourced from the following packages:

• Fingertips
• NHS-R Datasets
• MLDataR