Lec 0: Welcome + Course overview!

BIOS 600 - Spring 2026

Welcome! Meet Dr. Nate Wiecha

  • Postdoctoral research trainee, Department of Biostatistics, Joined September 2025
  • PhD in Statistics from North Carolina State University, 2025
  • Originally from Wellesley, MA. He/him.
  • Office Hours: Thursdays 9:45-10:45 AM and by appointment.

Meet the TA

  • HyungGyu Min
  • PhD student, Biostatistics department
  • Will lead lab sessions, Tuesdays 3:30-4:50, MHRC 0003
  • Additional TA office hours listed on syllabus.

Check out Ed Discussion

  • This semester, you can ask questions about the class in our discussion forum, Ed Discussion
  • The link to join is also posted on our home page in Canvas.
  • Take a moment to introduce yourself in the forum! Name, Year + Program, and a fun fact about yourself.

Meet each other

In groups of 4, share

  • Where you’re from
  • Your year/program
  • What you hope to get out of this course
  • Find 2 things that everyone in your group has in common, that isn’t necessarily common to the rest of the class.

Course overview: Canvas and course website

  • Canvas will have some course materials: syllabus, lab materials, assignment submissions
  • Course website will host syllabus, slides, assignments
  • Let’s take a tour!
  • More will be visible next week as first assignments are posted

Course toolkit

  • RStudio
    • Note: Next Tuesday in the first lab, TAs will help you download RStudio.
  • Assignment submission and feedback: Gradescope
  • Discussion forum: Ed Discussion

What have I gotten myself into?

  • BIOS 600 is an introduction to “principles of statistical inference” in biostatistics that:
    • provides a tour of basic statistical methods commonly encountered in public health and biomedical research,
    • emphasizes understanding of methods, using them to arrive at data-driven decisions, and effective communication of such results.
    • Utilizes modern software such as RStudio to reproducibly examine and manipulate data to make sound scientific conclusions

My goals in the course

  • Introduce you to basic biostatistics toolkit so that you can apply it to your own projects in the future, if you choose!

  • Build community!

Activities: Participate, Practice, Perform

  • Participate: Attend and actively participate in lectures and labs, office hours
  • Practice: Practice applying statistical concepts and computing with lab exercises (individual)
    • Review course topics with occasional participation exercises, typically a few questions administered via Canvas. Annotated notes will be uploaded after class.
  • Perform: Put together what you’ve learned to analyze real-world data
    • Homework assignments (individual)
    • Three in-class exams.

Cadence

  • Lecture: Tuesday/Thursday 8am-9:15am. Mostly lecture, sometimes a participation activity (could be a quick survey, or an activity to apply your knowledge). Lectures will not be recorded, but slides will be posted after each lecture.
  • Labs: Tutorial video and assignment will (typically) be posted Monday around noon. Start and make large progress on Tuesday in lab section, finish up by Friday 11:59pm of that week
  • HWs: Posted approx every 1-2 weeks, typically will have approximately 1 week to complete.
  • Exams: 3 in-class exams, more info as semester progresses.

What to expect in lab

  • Lab session will be Tuesdays from 3:30-4:50, MHRC 0003, led by TA

  • Instructions and tutorial video will be posted on Canvas on Monday by noon.

  • I know some of you have a conflict w/ lab time. That’s okay - attendance is not required. Watch the video and work on the lab, attend other scheduled office hours if you need help. Still turn in the assignment by Friday at 11:59pm of that week.

Tips

  • You do not have to finish the lab during the lab session, they will always be due the following Friday at 11:59 pm.

  • One work strategy is to get through portions that you think will be most challenging (which initially might be the coding component) during lab when a TA can help you on the spot and leave the narrative writing until later.

  • Use the time wisely to really learn the material and produce a quality report.

Grading

Category Percentage
Homework 30%
Labs 25%
Exams 40%
Participation 5%

See the course syllabus (.pdf available on Canvas) for how the final letter grade will be determined.

Textbooks

Recommended: (free online)

OpenIntro Statistics, Diez, Barr, Çetinkaya-Rundel, CreateSpace, 4th Ed. (2019)

R for Data Science, Wickham and Grolemund, O’Reilly Media, 1st Ed. (2017)

Optional: Principles of Biostatistics, Pagano and Gavreau, CRC Press, 2nd Ed. (2018)

Support

  • Attend office hours
  • Ask and answer questions on the discussion forum, Ed Discussion
  • Reserve email for questions on personal matters and/or grades
  • Additional support resources in the syllabus

Announcements

  • Posted on Canvas (Announcements tool) and sent via email, be sure to check both regularly
  • I’ll assume that you’ve read an announcement by the next “business” day

Diversity + inclusion

It is my intent that students from all diverse backgrounds and perspectives be well-served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that the students bring to this class be viewed as a resource, strength and benefit.

  • If you have a name that differs from those that appear in your official UNC records, please let me know!
  • Please let me know your preferred pronouns, if you want. You’ll also be able to note this in the Getting to know you survey.
  • If you feel like your performance in the class is being impacted by your experiences outside of class, please don’t hesitate to come and talk with me. I want to be a resource for you.
  • If you prefer to speak with someone outside of the course, your advisers and deans are excellent resources.

Diversity + Inclusion

I (like many people) am still in the process of learning about diverse perspectives and identities. If something was said in class (by anyone) that made you feel uncomfortable, please talk to me about it.

Course policies: Late work

  • HW & Labs: Deadlines are there to help you stay on track. However, I know that life happens! You have a 24-hour grace period to turn in any HW/Lab without penalty. No HW/Labs will be accepted after the 24-hour grace period, except in extenuating circumstances.

  • Lowest HW and Lab will be dropped.

  • No late work will be accepted for exams.

Collaboration policy

  • Only work that is clearly assigned as team work should be completed collaboratively.

  • Homeworks must be completed individually. You may not directly share answers / code with others, however you are welcome to discuss the problems in general and ask for advice.

Laptop policy

  • No open laptops during class.
  • You can take notes on a tablet or other horizontal screen, or using pen and paper.

AI policy

  • I (strongly) discourage the use of large language models (LLMs).

  • LLMs are unreliable, can be difficult to fact-check, and you will mentally engage less with the material using LLMs, making it less likely you’ll remember the material.

  • Exams will be in class, closed book.

AI for code

  • AI tools for code: You may make use of the technology for coding examples on assignments; if you do so, you must explicitly cite where you obtained the code, and share the AI prompt(s) you used.

  • Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism.

No AI for narrative

  • No AI tools for narrative: Unless instructed otherwise, AI is not permitted for writing narrative on assignments. In general, you may use AI as a resource as you complete assignments but not to answer the exercises for you. You are ultimately responsible for the work you turn in; it should reflect your understanding of the course content.

Sharing / reusing code policy

  • Unless explicitly stated otherwise, this course’s policy is that you may make use of any online resources (e.g. RStudio Community, StackOverflow, ChatGPT, etc.) but you must explicitly cite where you obtained any code you directly use or use as inspiration in your solution(s).

  • I know that you will have access to the internet and AI in the real world. However, you are required to understand (& be able to explain) every line of code you’re turning in.

  • Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism, regardless of source.

Most importantly…

Ask if you’re not sure if something violates a policy!

Three tips for success

I am here to help you succeed! :-)

  1. Ask questions.
  2. Do the homework and labs.
  3. Be proactive, even with the small questions!

Cultivating a supportive learning environment

I want to make sure that you learn everything you were hoping to learn from this class. If this requires flexibility, please don’t hesitate to ask.

  • You never owe me personal information about your health (mental or physical) but you’re always welcome to talk to me. If I can’t help, I likely know someone who can.
  • I want you to learn lots of things from this class, but I primarily want you to stay healthy, balanced, and grounded.

Questions

What questions do you have?

This week’s tasks

  • Read the syllabus! (a .pdf version is available on Canvas)
  • Keep an eye out for a Getting to Know You Survey via Canvas, I’ll send out an announcement.

I’m excited to begin this statistical journey with everyone!

Notecard

  • Write your name

  • What are you excited to learn in this course?