Comparison of R and Python Data Science Applications

Each of the sections labelled 1 to 8 below contains relevant information on the listed topics. These PDFs include written text, external links and coding examples with output. For a further breakdown of what can be found within these resources please read section 1, Introduction. To view raw research files (Jupyter Notebooks, etc.), please see the following Github page:

  1. Introduction
    • Links to external resources
    • YouTube videos
    • Tutorials
    • Reference manuals
  2. Mathematical Objects
    • Python NumPy Module, Vectors (1D Arrays), Matrices (2D Arrays)
  3. Mathematical Operations
    • Basic Vector Operations, Basic Matrix Operations, Basic Matrix-Vector Operation, Speeding up Matrix Multiplication
  4. Computing Least Squares Solutions
    • Ordinary Least Squares, Generalized Least Squares (GLS)
  5. Subtle but Important Differences
    • Initializing Objects in R, Initializing Objects in Python, R Pre-Allocation vs. Appending, Python Pre-Allocation vs. Appending
  6. Computing Statistics and Percentiles
    • Computing Basic Statistics, Computing Percentiles
  7. Data Visualizations and Plotting
    • R Plots and ggplot2 Package, Python Matplotlib Module, Scatter Plots, Histograms, Curves, Images and Array (Field) Plots, 3D Visualizations
  8. Predictive Models
    • Python pandas Module, Data, Regression, Decision Trees, Clustering, Time Series, Neural Networks in Python