Office Hours

Free drop-in office hours for help with data science 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.

Check our events calendar for scheduling changes.
There are no office hours on university holidays and academic breaks and during finals week.

Help Topics

We discuss theoretical, technical and applied issues in data science. Common questions 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!