Welcome to the Mass General Brigham (MGB) Python User Group
Python has been really popular in recent years not only of its powerful ability to manipulate data in a relatively easy and fast manner but also because of its statistical capability, and its ability to utilize a wide variety of packages for all tasks. Python is a programming language that lets you work quickly and integrate systems more effectively.
Promote and advance the use of Python and data science best practices at Mass General Brigham (MGB).
To advance Mass General Brigham (MGB) and our research community to be a leader in data-rich science by promoting the use and best practices of Python.
How to use Python at MGB
There are several different ways to use Python at at MGB
- MGB Jupyterhub Server
- MGB Jupyter provides the user-friendly interface to the users.
- Scientific Computing (SciC) Linux Clusters account is required for this service.
- Python in SciC Linux Clusters
- In SciC Linux Clusters settings, the users can utilize Python in a more flexible, distributed and heavy-computing way.
- SciC Linux Clusters account is required for this service.
- Python with IDEA platform
- IDEA platform is our newest big data platform.
- IDEA platform account is required for this service.
MGB Python User Group Meeting
MGB Python User Group Meetings are held at multiple locations to gather all MGB Python users - research scientists, clinicians, and administrators. In the meeting, the topics about training, Python-related infrastructure, and the policies in MGB Python settings are presented and discussed.
But most importantly, there are "show-and-tell" speakers to share their Python projects and there favorite tips and tricks.
(This is an informal event for employees who work within the MGB network.)
ERIS Scientific Computing offers Python training sessions. Please, check out the Events Calendar for current offerings and to register.
- Python-intro training
- Intermediate training
Intro to Python training is a one-day training course targeting research scientists who have no or little knowledge of Python (if you have used Python before, please do not sign up for this session). This hands-on training will provide many examples and exercises. Below are the topics that we will study:
- What is Python? Why Python?
- What to use?
- Python console, Python scripts, and Jupyter notebook
- How to read and write in Python
- Simple computation
- Data manipulation
- Data types
To facilitate an informal discussion between the members, we have created an e-mail discussion list where you can sign up here:
Python Training Notebooks
The attached Zip file on this web page contains Python worksheets separated out into folders that are ordered in numerical order. Each folder contains a worksheet that builds on the knowledge gained in the previous worksheet. Therefore it is advisable to review each worksheet in the order it has been laid out.
- Python Data Structures:
A solid foundation of built-in data types like List, Tuple, Dictionary, and Set are necessary before writing a single line of Python Code. This worksheet provides examples with detailed explanations as comments to firm up your knowledge.
- Conditional Logic & Loops
Learn how to program conditional logic and implement loop logic with any of the built-in data types while coding in Python. List and Dictionary comprehensions are included.
- Numpy Library
Learn about how to handle n-dimensional arrays. You will learn how to perform reshape, flatten, transpose, squeeze, slice, flip, sort, concatenate and arithmetic operations on arrays and learn how to manipulate images.
- Pandas Library
Learn how to handle tabular data using dataframes and learn advanced functions available in the library to manipulate tabular data. Learn how to display co-relation and co-variance matrix, sort, bin, aggregate, and connect to a Database source to pull data.
- Matplotlib Library
Learn more about how to use the plotting library available in Python for your data analysis projects.
- Sklearn library
Learn more about functions and packages available within the library for Classification, Regression, Clustering, Dimensionality Reduction, Model selection, and pre-processing. It also takes you through an immersive experience with a sample data science project’s life cycle in 6 stages.
Notebooks provided by: Eby Kuriakose, Physician Analytics & Business Intelligence, MGB