Skip to content

Latest commit

 

History

History
23 lines (20 loc) · 1.62 KB

README.md

File metadata and controls

23 lines (20 loc) · 1.62 KB

Comparison of R and Python Data Science Applications

The attached files are related to the following Math Faculty Computing Facility link:
https://uwaterloo.ca/math-faculty-computing-facility/comparison-r-and-python-data-science-applications

Each of the sections labeled 1 to 8 contain relevant information on the topics listed below. 'PDF', 'HTML' and 'JNB' (Jupyter Notebook) folders contain the relevant files for each of the sections 1 to 8. Please note that the 'Extras' folder contains additional jpeg images as well as two small csv files. These may be needed for those who wish to run certain Jupyter Notebook files.

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