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