Are you ready to take the first steps in using code to work with data? In this training, you’ll learn how to use Python, one of the most popular programming languages, to analyze and visualize data. This training welcomes first-time coders and Python beginners, so we’ll begin with Python programming fundamentals before moving on to the basics of data analysis and visualization later in the training.

• Describe and use basic Python concepts (e.g., strings, integers, printing, variables, lists, dictionaries, loops, conditional logic, etc.)
• Handle mistakes in coding
• Use add-on modules (I.e., import libraries) for Python, including numpy and matplotlib
• Read data into Python
• Perform basic statistical operations on data
• Create basic plots from data

This training will be hands-on; come ready to code alongside the instructor. Details about technical setup will be emailed to registrants a few days before the course begins. Please note that registration is required for this event.

The hands on portion is located in the python1_2025_workshop.ipynb jupyter notebook file. This file can be viewed in github or your preferred jupyter notebook environment. colab.research.google.com is utilized in the class.

Some of this material, including the inflammation data, is adapted from the Carpentries, using a CC-BY-4.0 license.

Course material adapted from:

• https://swcarpentry.github.io/python-novice-inflammation/index.html

These libraries form a powerful toolkit for data analysis in Python. While each serves a distinct purpose, they are frequently used in conjunction, with later libraries often leveraging the capabilities of the earlier ones.

• Python:

  https://colab.research.google.com/github/jakevdp/WhirlwindTourOfPython/blob/master/Index.ipynb

• Numpy:

  https://numpy.org/doc/stable/user/quickstart.html

• Matplotlib:

  https://matplotlib.org/stable/users/explain/quick_start.html

• Pandas 10 min intro:

  https://pandas.pydata.org/docs/user_guide/10min.html

• LinkedIn Learning Course on Python Data Analysis:

  • https://www.linkedin.com/learning/python-data-analysis-24296803/from-data-to-insight-with-python

• Official Python docs: https://python.org
• Beginner guide: https://wiki.python.org/moin/BeginnersGuide
• Cheatsheets:
• Python: https://www.pythoncheatsheet.org/ + https://www.datacamp.com/cheat-sheet/getting-started-with-python-cheat-sheet
• See more helpful learning resources (under Python): https://library.medicine.yale.edu/research-data/learn-work-data

Each library we used today has its own documentation, for further reading.

• numpy — docs | user guide
• matplotlib — docs | user guide | cheatsheets | Matplotlib Tutorial
• glob — docs

• markdown – guide | cheatsheet
• Seaborn Color Palettes - PyData Tutorial

https://research.computing.yale.edu/training/python-series