Office Hours

DataLab offers free drop-in office hours during which University researchers can obtain data science help and advice on working with data. During these short, just-in-time sessions we meet with faculty, students, postdoctoral scholars, and staff from all domains across the University. Depending on the nature of your question and our availability, it may be possible to schedule a longer follow-up consultation after your office hours visit.

There are no office hours on University holidays and academic breaks and during finals week.

DataLab In-Person Office Hours

Spring 2024 – Tuesdays, 1:00 PM – 2:00 PM in Shields Library, room 360*

Complete the intake form to get help. Drop-ins welcome. Directions to DataLab.

Come ask the DataLab your pressing data science questions.

Data Science Collaborative Virtual Office Hours

Spring 2024 – Mondays, 1:00 PM – 2:00 PM on Zoom*

Register for the link to join

Technical experts from around the University are available to answer your research computing questions. This event is supported by: CTSC Biostatistics, Computational Research Service, DataLab, Library, High Performance Computing Core Facility, and Stat Lab.

Help Topics

We discuss theoretical, technical, and applied issues in data science. Common questions we see include, but are not limited to:

  • Reproducible and responsible research computing and data science
    • Programming (R, Python, JavaScript, Julia)
    • Collaboration and workflows (Jupyter notebooks; text editors; plugins and customization; SSH and remote logins)
    • Version control and reproducibility (Git; GitHub; Docker)
  • Data gathering and organization
    • Experimental and survey design
    • Tidy practices
    • Databases (MySQL Workbench; SQLite; DB Browser; NoSQL; Postgres; Solr)
    • Web and PDF scraping
    • Optical Character Recognition
  • Data analysis and exploration
    • Machine learning and neural networks (TensorFlow; Pytorch)
    • Natural Language Processing (NLP) and Text Mining
    • Network Science
    • Frequentist and Bayesian statistics in R
  • Data visualization
    • static and dynamic (ggplot; plot.ly; RShiny; etc.)
    • 3D (Virtual Reality User Interface)
  • Machine learning and neural networks (TensorFlow; Pytorch)
  • Intensive and efficient computing
    • Virtual machines and installing additional operating systems
    • GPU toolkits (CUDA, OpenCL)
    • Cluster and High Performance Computing
    • Development Tools (compilers; linkers; profilers; configure; make; cmake; snakemake)
  • … and more!

Note: We are unable to provide support for assigned coursework (homework assignments, class projects, etc.) during office hours. If you need such help please contact your instructor or TA for assistance. If you need help with technical problems such system administration and hardware issues, check out the the UC Davis ServiceHub and/or contact your departmental IT.

ADDITIONAL RESOURCES

If you are looking to learn more about data science check out DataLab’s upcoming and archived workshops, and list of courses at UC Davis. We also support Research and Learning Clusters on various data science topics, including user groups for R and Python.

For more acute assistance, check out the following recharge services:

  • Stat Lab: The Stat Lab offers help and hosts office hours for statistical modeling and machine learning.
  • Biostatistics Office Hours: Run through the Clinical and Translational Science Center at UCDH, Biostats holds office hours for medical researchers.

And, we’d be remiss if we didn’t mention Stack Overflow – an online hub for programing help. Search for your problem or error and you are likely to find a solution.