Research Guides: NBIO 208: Home

Home

Science Services from Brandeis Library

Scientific Computing Guide
Find more resources about research management and programming languages, or connect to a free consultation at the Science Services guide.

Interactive Tutorial: Statistics as Linear Models with Python

Here are three ways to access your Jupyter Notebook for statistics in python:

Access on Google Colab. Click "Copy to Drive" to save your own version and try the practice problems and challenge.
Download Jupyter Notebook from Google Drive. Available to Brandeis University. You can work with the Jupyter Notebook file (.ipynb) in your own environment.
View rendered Jupyter Notebook. This version is not interactive.

Topics include:

The simplicity underlying common statistical tests
The python environment
Correlations
One mean comparison
Many mean comparisons
Checking assumptions

New to Google Colab? Check out this 3 minute summary from Google.

Getting started with Google Colab

Getting Started with Python

What is Python?

Python is a high-level and general-purpose programming language. As this definition implies, you can use Python for several purposes, from web development to data science, machine learning, and robotics.

Installing Python

Python 3 has many popular scientific libraries. There are many ways to install Python, but installing Anaconda ensures the installation of key libraries and environments. Detailed installation instructions for various operating systems can be found on The Carpentries template website for workshops and in Anaconda documentation.

Cloud-based Python

Google Colaboratory
Cloud-based python in Colab notebooks allow you to combine executable code and rich text in a single document, along with images, HTML, LaTeX and more.

Introductory Tutorials

Introduction to Python Programming
This 3-workshop hands-on series is for Brandeis community members who are beginners in python and want to start writing their own code. The best way to learn how to program is to do something useful, so this introduction to Python is built around a common scientific task: data analysis.
Kaggle Learn Python Course
Seven interactive lessons and exercises for getting started with syntax, functions, conditionals, and more. Set up a free account.

More Resources

PyMC
Of particular interest for Bayesian modeling is PyMC, which implements a probabilistic programming language in Python.
Python Programming for Science guide
More resources and workshop materials from Data Services.
Scipy Lecture Notes: Statistics in Python
Think Stats 2e
Think Stats is an introduction to Probability and Statistics for Python programmers.
Neuroscience, Stats, and Coding Blog