This course provides an introduction to Python, and was created with support from Texas A&M Institute of Data Science and the Texas A&M University Department of Mathematics. For this course I provide Jupyter Notebooks demonstrating the material and provide links to additional resources, and I also provide a number of short videos, corresponding to the notebooks providing additional explanation.
After completing this primer you should be able to do the following:

• Learn where to find tutorials, documentation on Python as well as community developed packages.
• Use comments and learn how to incorporate them into the help system.
• Write and evaluate expressions using variable names.
• Use basic data structures including strings, lists, tuples, sets, and dictionaries, among others.
• Understand and use various control flow mechanisms, including looping, functions and exceptions.
• Write functions, understand return values and argument lists, including positional and keyword arguments.
• Understand binding and copies, as well as interning/small integer caching.
• Learn about name resolution, blocks, scope and name spaces.
• Find and use modules and packages.
• Learn about objects, classes and inheritance, including multiple inheritance.

This course provides an introduction to R, and was created with support from Texas A&M Institute of Data Science as part of the Biomedical Data Science Online Training Program. For this course I provide Jupyter Notebooks demonstrating the material and provide links to additional resources, and I also provide a number of short videos, corresponding to the notebooks providing additional explanation.
After completing this primer you should be able to do the following:

• Create and manage R projects using the R console, RStudio, RStudio Cloud, Google Colab, and Jupyter Notebook
• Use the built-in help system to find details on functions, packages and data sets
• Write and evaluate expressions using variable names
• Understand name resolution and conflicts
• Use basic data structures including vectors, lists, factors and data frames, among others.
• Understand and use various control flow mechanisms, and how to replace looping structures using apply
• Write functions, understand argument lists, use named and unnamed arguments including default values
• Understand and utilize short-circuit and lazy evaluation
• Find, install and load packages from CRAN
• Read/Write basic custom data files
• Visualize data using ggplot and other plotting tools

The Texas A&M Institute of Data Science sponsored a five-day hands-on data science bootcamp August 8-12, 2022. This camp provided students and researchers an introduction to the fundamentals of data science, including Software Carpentry, Introduction to Python, Data Science Primer, and Fundamentals of Deep Learning.

Session 1b: Intro to Python, Matthew Hielsberg
This one day session provided instruction and hands-on training in Python using Google Colab.

GitHub: hielsber-tamu/tamids-ds-bootcamp-python-0822

Open Source for Open Science (OSOS) is a free workshop sponsored by the Department of Ecology and Evolutionary Biology and is aimed to familiarize participants with selected open source software and programming tools to be able to conduct quantitative analysis and statistical methods for scientific inquiry.

Session 4: Data Analyses in Python, Matthew Hielsberg
This three hour session provided instruction and hands-on training using Jupyter Notebook and Google Colab.

Topics covered during the three hour session:

• Jupyter Notebook and Google Colab
• Python:
  • What is Python?
  • Help System
  • Lists, Strings, Tuples, Sets, Dictionaries
  • if, else, for, while, break, continue, pass
  • Functions, Arguments, return, Aliasing
  • Comprehensions
  • NumPy, SciPy, Pandas, rpy2
  • Series, DataFrame
• Reading/Writing Data
• Data Cleaning
• Visualization with Matplotlib, Seaborn and Plotnine
• Side-by-side examples in Python and R

Goals and Scope: Technology Services and Red Hat sponsored a two day in-house training event covering Ansible, Ansible Automation Platform and Satellite. Presenters for the sessions were Nathan Goodnight and Michael Quick from Red Hat.

Monday, July 10 (10am - 2pm, O&M 203): This session focused on exploring Ansible Automation Platform and how TAMU can best utilize its functionality. Areas of focus:

• Red Hat Ansible overview, YAML, playbooks (creation, maintaining, verification, testing), etc.
• Ansible use cases: infrastructure as code, config management, compliance, automation of manual tasks, etc.
• Ansible best practices, reusable patterns and processes across various departments/groups
• Additional Ansible resources: Red Hat, open source community, Galaxy, Content Hub, etc.
• Q&A and participant-suggested areas of focus

Tuesday, July 11 (10am - 2pm, O&M 203): This session focused on exploring Satellite and how TAMU can best utilize its functionality in light of the new RHEL environment. Areas of focus:

• Satellite Setup / Installation.
• Red Hat best practices for infrastructure management, scalability, multiple managed content repos with varying lifecycles, patching RHEL (and other) servers, managing large numbers of servers.
• Provisioning of physical and virtual servers (web and using API/Ansible).
• Subscription management (certificates, manifests, multiple Satellite instances).
• Access control options, reporting capabilities.
• Integration with Ansible Automation Platform.
• Q&A and participant-suggested areas of focus.

Organizers:

• Matthew Hielsberg (Technology Services, TAMU)
• Chris Mouchyn (Technology Services, TAMU)
• Nathan Goodnight (Red Hat)

Goals and Scope: The Texas A&M Institute of Data Science (TAMIDS) is hosting a one-day workshop to bring together TAMU researchers across different colleges and disciplines with common interests in the intersection of Uncertainty Quantification and Machine Learning. The multidisciplinary interactive workshop will feature ranges of topics from theory to applications, and offer a venue to potentially seed new collaborations and help strengthen TAMU's competitive position in the field through presenting research results, sharing open problems, and demonstrating tools and software in machine learning and artificial intelligence as these pertain to uncertainty quantification and probabilistic computational methods.

Organizers:

• Matthew Hielsberg (Institute for Scientific Computation and Department of Mathematics)
• Arash Noshadravan (Department of Civil and Environmental Engineering)
• Rui Tuo (Department of Industrial and Systems Engineering)