Update README.md

Author: slmcconv

README.md

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-# Comparison of R and Pythondata science applications
+# Comparison of R and Python Data Science Applications
 
-see some into file
+Each of the sections labeled 1 to 8 contain relevant information on the listed topics. This includes written text, external links and coding examples with output. For a further breakdown of what can be found within these resources please read the section 1 Introduction. PDF, HTML and Jupyter Notebook files are available for each of the sections 1 to 8.
+
+### 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
+
+Link: https://uwaterloo.ca/math-faculty-computing-facility/comparison-r-and-python-data-science-applications