Development Plan
(Image credit: Charles Deluvio, Unsplash)
UA Data Lab Consultations Logs
The DSI is launching the Data Science Lab (DSLab) in 3 months, to offer consulting services to the UA research community in Deep Learning applications.
The DSLab will consist of a team of 2 Educators, plus a group of 4 graduate students that will collaborate on different research projects, in which they will use real-world data science tools and develop the necessary skills to go out and be inserted in new data science-related projects.
A general scheme for the launch of the new DSLab is needed and a detailed description of the timeline and tasks are required for the preparation and at least the first year of operations.
A plan for training and skill development in deep learning tools and algorithms is needed.
This phase will focus on defining the scope of the DSLab, hiring a team, developing a training plan, establishing partnerships, developing policies and procedures, and launching the DSLab's consulting services.
- [] Define the scope of the DSLab
- [] Inquiry for funds needed to hire 4 graduate students and hire them
- [] Develop a plan for training and skill development in deep learning libraries and algorithms
- [] List possible collaboration partnerships within the UA research community
- [] Define the goals and objectives of the DSLab
- [] Develop a promotional strategy to create awareness about the DSLab among the research community
- [] Define the organizational structure and management team for the DSLab
- [] Develop and finalize the DSLab policies and procedures for its operation
- [] Define the governance for the DSLab
- [] Develop the infrastructure and equipment requirements for the DSLab
- [] Define required physical space for the DSLab
- [] Establish the training program for the Fall 2023 semester
- [] Develop a communication plan for the DSLab opening
- [] Conduct a soft launch of the DSLab within the research community
- [] Define DSLab consulting requests according common DSI procedures
- [] Conduct a trial run of the DSLab's consulting services
This phase will focus on engaging with UA research community expanding services, conducting a review of performance, and expanding consulting services to other areas within data science.
- [] Launch the DSLab's consulting services to the research community
- [] Continue refining and improving the DSLab's training program based on feedback from the community
- [] Establish the feedback mechanism to continuously improve DSLab's consulting services
- Expand services to different groups in the research community
- Engage in promotion activities of data science education in undergrad and graduate students
- Conduct continuous review of the DSLab performance and identify areas for improvement
- Expand the DSLab's consulting services to other areas of data science to include deep learning and AI applications
- Continue promoting internships to increase student participation in the DSLab activities
- Collaborate in a data science competition for the UA research community
- Define required space for the DSLab team and services
- Evaluate the DSLab success over the first year and plan future activities and growth
- Participate/collaborate in formal programs for data science skills (DSF, R4R, FOSS, ...)
- Create a DSLab webpage promoting consulting services
- Participate in weekly Data Science Workshops:
- Python for Data Science
- Classical Machine Learning
- Data Science Tapas
- Prompt Engineering & AI Tools
- Attend normal consultation requests by grad students & postdocs
- Define DSLab trainings and tools in: Deep Learning theory, tools and algorithms
- Create a knowledge base for assimilating Deep Learning Applications (LLMs, HuggingFace, OpenCV, Fast.ai, PyTorch, TensorFlow, ...)
- Create a collection of Jupyter Notebook cases/examples in Deep Learning
- Launch an internal regular Deep Learning Applications workshops using CyVerse
- Hiring, Managing, and Retaining Data Scientists and Research Software Engineers in Academia: A Career Guidebook from ADSA and US-RSE, Van Tuyl, Steve Editor. August 18, 2023.
Created: 06/11/2023; Updated: 06/23/2023
Carlos Lizárraga
University of Arizona. Data Science Institute, 2023.
