ST 811 Navigating the PhD program and beyond: perspectives, skills, and strategies

Semester: Fall 2026

Lecture days/times: 15:00–16:15 Wednesdays

Location: 1218 SAS Hall

1 credit hour

Instructor

Course Description: This course will expose PhD students in statistics to a structured overview of the skills they are expected to learn, how they are expected to develop these skills, what type of career opportunities they are being trained for, and how to best align with a particular career path. The course will focus on the skill development necessary for research-focused academic, teaching-focused academic, and industry career paths. The course will mainly focus on the following aspects, in particular: beginning in research in statistics, statistics research landscape, professional development, scientific writing, research, teaching, and job searches. The course will involve guest speakers, individual presentations, and class participation.

Prerequisites: This course is intended for second year PhD students in statistics.

Learning Outcomes. By the end of the course, students will be able to:

1. Enumerate eight relevant aspects to consider for finding a dissertation advisor.
2. Categorize six types and numerous areas of statistics research.
3. Explain the different roles of Monte Carlo simulations versus mathematical proofs.
4. Summarize technical research articles in four levels of depth.
5. Outline the typical structure of a statistics research article.
6. Write a technical literature review.
7. Formulate ideas for open source research practices.
8. Prepare slides for technical presentations for seminar (long) versus conference (short) talks.

Optional Texts:

• How to succeed in academics by Linda McCabe and Edward McCabe
• The academic job search handbook by Julia Vick and Jennifer Furlong
• The professor is in: the essential guide to turning your PhD into a job by Karen Kelsky
• Preparing for a career in academia: personal stories, insights, and advice by Dana Dunn and Maureen McCarthy
• Becoming an academic: how to get through grad school and beyond by Inger Mewburn, Katherine Firth, and Shaun Lehmann
• The Chicago guide to your academic career: a portable mentor for scholars from graduate school through tenure by John Goldsmith, John Komlos, and Penny Gold
• How to write mathematics by Paul Halmos
• Writing mathematical papers in English: a practical guide by Jerzy Trzeciak
• Mathematical writing by Donald Knuth, Tracy Larrabee, and Paul Roberts
• How to write and publish a scientific paper by Robert Day and Barbara Gastel

These books focus on the practical aspects of being a successful academic, such as navigating the academic job market, publishing and grant writing, and teaching strategies. They also provide personal stories and advice from experienced academics, making them useful resources for PhD students preparing for academic careers.

Grade Distribution:

Attendance	50%
Assignments	50%

Grade Distribution:

≥ 70.00

Satisfactory (S)

≤ 69.99

Unsatisfactory (U)

No expectations beyond attendance apply to students choosing to audit the course.

Final period: 15:30–18:00 Friday, 4 December 2026 in 1218 SAS Hall

Course policies and commentary:

• Assignments
  • Several homework assignments to carry out the course learning outcomes (listed above) will be assigned throughout the semester. Assignments and their due dates will be posted on the course website, linked above. Assignments will generally be due 2–3 weeks after assigned/posted.
  • The instructor will grade the assignments based on completion. Each assignment counts equally in its contributions to the total course assignments grade.
  • No late assignments will be accepted, but arrangements will be made on an individual basis for students who experience prolonged health or other issues.
• Attendance
  • Attendance is recorded. Please confirm your attendance is recorded by the instructor each lecture, beginning on the second lecture of the semester. Each lecture counts for 1 point of attendance, and the total course attendance grade is the average of points across all lectures, excluding the first lecture. Arrangements will be made on an individual basis for students who experience prolonged health or other issues.
  • Use lecture time as you feel most productive, but do not use it in a way that is distracting to others.
• Class recording statement
  • No components of this course will be recorded.

Tentative Course Outline:

• Transitioning from coursework to research (1 week)
• Areas of statistics research (1 week)
• Monte Carlo simulation studies versus mathematical proofs (1 week)
• Scientific writing: general principles (1 week)
• Scientific writing (and reading): literature reviews (1 week)
• Scientific writing: academic publishing and the purpose of journals (1 week)
• Open source research practices (1 week)
• Academic presentations (1 week)
• Academic careers (1 week)
• Academic research careers at R1 institutions (1 week)
• History of statistics (1 week)
• Philosophy of science (1 week)

NCSU Policies, Regulations, and Rules:

Students are responsible for reviewing the NC State University Policies, Rules, and Regulations (PRRs), which pertain to their course rights and responsibilities, including, but not limited to:

• POL 04.25.05 Equal Opportunity and Non-Discrimination Policy Statement
  • REG 02.20.01 Academic Accommodations for Students with Disabilities
• POL 11.35.01 Student Conduct
  • REG 11.35.05 Code of Student Conduct
  • REG 11.35.02 Student Discipline Procedures
• REG 02.20.03 Attendance Regulations
• REG 02.50.03 Grades and Grade Point Average
• REG 02.20.15 Credit-Only Courses
• REG 02.20.04 Audits
• REG 08.00.11 Online Course Material Host Requirements

This course engages diverse scholarly perspectives to develop critical thinking and analysis. A debate and inclusion of a reading or media does not imply endorsement. A syllabus does not constitute an express or implied contract among the student, faculty, or institution. Rather, it serves as a guide for the course. This syllabus may be modified according to course needs.