Data Science Notes

Status: In Progress

To Do

• Complete Setup Walkthrough
• Add SKlearn Walkthrough
• Add SQL Walkthrough

Purpose

The purpose of this collection of notebooks is to house my notes as they relate to data analytics, statistics, and data science.

Technologies

• Python (v3.7)
• IPython
• Jupyter Notebooks

Required Libraries

• IPython
• Jupyter Notebooks
• Pandas
• Numpy
• Matplotlib
• Seaborn
• Scipy
• Scikit-Learn

Project Description

This project ultimately serves to house the notes outlining several topics as they relate to the modern data environment. Contained within this project are several jupyter notebooks, each of which covers a broad technology or library that is regulary implemented in interacting with data. The data and img directories contain datasets and images, respectively (and obviously), that are utilized in the demonstrations of each workbok.

Below is a brief overview of each notebook and their intended purpose. I'd recommend viewing them in the order presented, as generally the information presented builds off of previous notebooks.

Notebook Sections

1. Set Up

This notebook introduces the steps one would generally take to setup a project. These topics include virtual environments, github, and markdown notation.

2. SQL

This notebook summarizes useful and the most common functions for interfacing with databases using SQL syntax.

3. Pandas

This notebook introduces the most widely used tabluar data manipulation library, Pandas.

4. Matplotlib

The purpose of this notebook is to build a foundation of the common visualization library, MatPlotLib.

5. Seaborn

With the foundation for MatPlotLib laid, I will now overview the Seaborn library which simplifies visualizations in Python.

6. Stats

This notebook serves to overview statiscal testing procedures in Python.

7. Regressions

The purpose of this notebook is to demonstrate how to construct traditional statistical regressions in Python.

8. Machine Learning

This notebook serves to introduce machine learning methodologies to create models in Python.

9. Neural Networks

This notebook shows how to build simple neural networks for modeling and classification.

Directories

data

Data imported and exported throughout the above notebooks are stored here.

img

Images used for illustrative purposes are saved here.

pdf