FAQ

Do I need prior experience with programming or data science?

No prior programming experience is required. The course builds from fundamentals and offers guided support. However, familiarity with Excel, basic statistics, or biological/agricultural concepts will be helpful.

What software will we use?

We will primarily use:

  • Tableau for interactive dashboards.
  • R or Python (depending on the specific course structure) for data analysis.
  • Spreadsheet tools such as Excel or (less preferred) Google Sheets.
  • Additional tools may include farm sensor platforms or datasets commonly used in applied agriculture research.

How do I access Tableau for this course?

You will receive a free student Tableau license through the Tableau for Teaching program. Licenses are typically issued about one week before the course start date. If you do not receive your license, check spam and contact the instructor.

What are the expectations around AI tools like ChatGPT?

CALS guidelines encourage instructors to define clear expectations around AI use (referenced in Practical AI Resources for your Courses). In this course:

  • AI may be used to support idea generation, debugging, and improving clarity of writing.
  • AI may NOT be used to generate full assignments, analyses, or dashboard interpretations.
  • Any AI use must be disclosed in your submission.

A detailed AI policy is posted in the syllabus.

Will we use real dairy farm data? Is it confidential?

Yes, we will work with anonymized agricultural datasets. Data confidentiality rules will be explained in class — typically aligned with research ethics guidelines and institutional standards.

How much time should I expect to spend on the course each week?

Expect approximately:

2–3 hours of lectures or guided sessions (LEC & DIS) 3–5 hours of hands‑on data practice or assignment work (LAB time) Additional time as needed for the final project

What if I need help or get stuck?

You are encouraged to:

  • Directly contact with the instructor or TA () office hours
  • Ask questions during lectures and discussion sessions
  • Reach out by email for technical or conceptual help

How will my work be evaluated?

Assessment typically includes:

  • Weekly or bi‑weekly coding assignments
  • Coding exam at the end of the semester
  • Final data‑driven project