Introduction to Python for Data Science

This is the ODSC West '17 workshop Introduction to Python for Data Science.

Outcomes

Attendees will come away with knowledge about how to effectively use Python and packages in the PyData stack like pandas, matplotlib, and scikit-learn to do data science. We'll learn how to get and read data, clean and munge data, conduct exploratory data analysis, and use scikit-learn to do machine learning with a real-world data set. We will focus on best practices in using Python along the way.

Pre-Requisites

Some very basic knowledge of Python will be helpful. If you have never used Python before, but have programmed in some other language, that's fine. If you don't have a lot of experience with programming, you may want to look at at least the first five sections of the python tutorial. Other than the basics of Python, no familiarity with Python tools will be expected.

Some knowledge of statistics and data science will be helpful.

Requirements

If you would like to code along during the workshop, follow these steps.

Clone this repository. To do so at the command line, type

git clone git@github.com:jseabold/odsc-west-python-2017

Otherwise, you can download the code directly from GitHub.

The day before the conference, you will want to check for any code changes. From the directory that containers the code, run

git pull

Before you arrive, also install all of the pre-requisite packages. You may not be able to rely on conference Wi-Fi to do this.

conda env create -f environment.yaml
source activate odsc-west-2017

Make sure you can run the Jupyter Notebooks

jupyter notebook

Make sure that all of the packages were installed correctly.

python imports-test.py

Outline

Introduction

What's data science? Why Python? What does it mean to write Pythonic code?

Geting and Reading Data with Python

Working with CSVs, json, web data using pure Python and pandas.

Data Wrangling with Pandas

How can I use pandas to clean my data?

Exploratory Analysis with Pandas

How can I use pandas to learn more about data?

Exploratory Plotting

How can I use Python plotting libraries like matplotlib and seaborn to understand the structure in my data?

Scikit-Learn

How can I put all of this together and build a machine-learning pipeline on some real world data?