Past Events

The UT Libraries are excited to announce an Immersive Python Workshop August 12-14, 2026! This in-person workshop will take place in the PCL Scholars Lab. It is aimed at UT students, faculty, and staff. You don't need to have previous knowledge of Python to participate in this workshop. The workshop will start with an introduction to Python, and then participants will choose a particular track (geospatial analysis, sentiment analysis, or data visualization) to learn more about more specific applications of Python. The workshop will conclude by walking all participants through the process of openly sharing code through GitHub. Short tutorials alternate with hands-on practical exercises. Participants are encouraged to help one another and apply what they learn to their own research problems during and between sessions. The workshop will run for three days. Day 1 is optional and recommended for participants who are new to Python or have limited experience with programming and would like an introduction to terminology and help with software installation. To register, visit the Immersive Python Workshop LibGuide and click the registration button. Registration will open on July 22 at 12:00 pm. Space in this in-person workshop is limited, so please register early. Day 1 (Optional) Wednesday August 12, 1:00 pm - 3:00 pm CT Creating variables and changing variables How to use a function How to get help while learning to program Basic data types in Python Day 2 Thursday August 13, 12:30 pm - 4:00 pm CT Intro to Python basics Python and APIs for retrieving data File Management Python & VS Code installation Day 3 Friday August 14, 12:30 pm - 4:00 pm CT Sentiment Analysis or Geospatial Analysis or Data Visualization GitHub for open-source Python software AI and Python For more information/questions, please contact Anna McGilvray, annamcgilvray@austin.utexas.edu

The UT Libraries are excited to announce an Immersive Python Workshop August 12-14, 2026! This in-person workshop will take place in the PCL Scholars Lab. It is aimed at UT students, faculty, and staff. You don't need to have previous knowledge of Python to participate in this workshop. The workshop will start with an introduction to Python, and then participants will choose a particular track (geospatial analysis, sentiment analysis, or data visualization) to learn more about more specific applications of Python. The workshop will conclude by walking all participants through the process of openly sharing code through GitHub. Short tutorials alternate with hands-on practical exercises. Participants are encouraged to help one another and apply what they learn to their own research problems during and between sessions. The workshop will run for three days. Day 1 is optional and recommended for participants who are new to Python or have limited experience with programming and would like an introduction to terminology and help with software installation. To register, visit the Immersive Python Workshop LibGuide and click the registration button. Registration will open on July 22 at 12:00 pm. Space in this in-person workshop is limited, so please register early. Day 1 (Optional) Wednesday August 12, 1:00 pm - 3:00 pm CT Creating variables and changing variables How to use a function How to get help while learning to program Basic data types in Python Day 2 Thursday August 13, 12:30 pm - 4:00 pm CT Intro to Python basics Python and APIs for retrieving data File Management Python & VS Code installation Day 3 Friday August 14, 12:30 pm - 4:00 pm CT Sentiment Analysis or Geospatial Analysis or Data Visualization GitHub for open-source Python software AI and Python For more information/questions, please contact Anna McGilvray, annamcgilvray@austin.utexas.edu

The UT Libraries are excited to announce an Immersive Python Workshop August 12-14, 2026! This in-person workshop will take place in the PCL Scholars Lab. It is aimed at UT students, faculty, and staff. You don't need to have previous knowledge of Python to participate in this workshop. The workshop will start with an introduction to Python, and then participants will choose a particular track (geospatial analysis, sentiment analysis, or data visualization) to learn more about more specific applications of Python. The workshop will conclude by walking all participants through the process of openly sharing code through GitHub. Short tutorials alternate with hands-on practical exercises. Participants are encouraged to help one another and apply what they learn to their own research problems during and between sessions. The workshop will run for three days. Day 1 is optional and recommended for participants who are new to Python or have limited experience with programming and would like an introduction to terminology and help with software installation. To register, visit the Immersive Python Workshop LibGuide and click the registration button. Registration will open on July 22 at 12:00 pm. Space in this in-person workshop is limited, so please register early. Day 1 (Optional) Wednesday August 12, 1:00 pm - 3:00 pm CT Creating variables and changing variables How to use a function How to get help while learning to program Basic data types in Python Day 2 Thursday August 13, 12:30 pm - 4:00 pm CT Intro to Python basics Python and APIs for retrieving data File Management Python & VS Code installation Day 3 Friday August 14, 12:30 pm - 4:00 pm CT Sentiment Analysis or Geospatial Analysis or Data Visualization GitHub for open-source Python software AI and Python For more information/questions, please contact Anna McGilvray, annamcgilvray@austin.utexas.edu

Spend a week with TACC HPC experts learning how to effectively use supercomputing for research and code development. The course introduces core parallel programming paradigms used in HPC, including CUDA, MPI, OpenMP, and OpenMP offloading, along with essential HPC tools. Participants complete hands-on labs using TACC systems and can attend additional sessions on Julia, modern C++, and the CMake ecosystem for scientific software development.

An immersive dive into machine learning best practices and applications in life sciences. This week-long in-person workshop guides participants through machine learning fundamentals up to cutting edge deep learning tools and methodologies for implementation in life sciences research. Attendees will also gain hands-on experience utilizing data from the National Institutes of Health's Common Fund Data Ecosystem, learn effective techniques to prepare and manage preprocessing of datasets, and learn how to train and deploy their own models efficiently and accurately.

An immersive dive into machine learning best practices and applications in life sciences. This week-long in-person workshop guides participants through machine learning fundamentals up to cutting edge deep learning tools and methodologies for implementation in life sciences research. Attendees will also gain hands-on experience utilizing data from the National Institutes of Health's Common Fund Data Ecosystem, learn effective techniques to prepare and manage preprocessing of datasets, and learn how to train and deploy their own models efficiently and accurately.

An immersive dive into machine learning best practices and applications in life sciences. This week-long in-person workshop guides participants through machine learning fundamentals up to cutting edge deep learning tools and methodologies for implementation in life sciences research. Attendees will also gain hands-on experience utilizing data from the National Institutes of Health's Common Fund Data Ecosystem, learn effective techniques to prepare and manage preprocessing of datasets, and learn how to train and deploy their own models efficiently and accurately.

An immersive dive into machine learning best practices and applications in life sciences. This week-long in-person workshop guides participants through machine learning fundamentals up to cutting edge deep learning tools and methodologies for implementation in life sciences research. Attendees will also gain hands-on experience utilizing data from the National Institutes of Health's Common Fund Data Ecosystem, learn effective techniques to prepare and manage preprocessing of datasets, and learn how to train and deploy their own models efficiently and accurately.

An immersive dive into machine learning best practices and applications in life sciences. This week-long in-person workshop guides participants through machine learning fundamentals up to cutting edge deep learning tools and methodologies for implementation in life sciences research. Attendees will also gain hands-on experience utilizing data from the National Institutes of Health's Common Fund Data Ecosystem, learn effective techniques to prepare and manage preprocessing of datasets, and learn how to train and deploy their own models efficiently and accurately.

This five-day course will introduce students to basic concepts in programming using the Python language, establishing a foundation for scientific computing. Trainees will learn introductory topics such as data structures, control flow, functions, file input/output, and data parsing. The class will work with SciPy libraries like Pandas. Trainees will have full access to the teacher’s course book and course content (datasets, scripts, and jupyter notebooks).