The foundational pillars of this Spring term course were laid by my esteemed mentors and colleagues in the Department of Surgery at Hopkins, Dorry Segev and Allan Massie.

Quick References#

Classroom and Virtual Attendance Information#

  • Location: Wolfe W1214 (Sheldon Hall), East Baltimore Campus. You’re encouraged to join the in-person experience for enriched learning.

  • Zoom Online Classroom: Join Zoom Meeting. Active participation in Zoom sessions is highly encouraged. Ask questions, share your screen when troubleshooting, and engage with your peers to mimic the in-person classroom environment as closely as possible.

    • Sessions and labs will be recorded and stored in the cloud for 180 days for review and reference. Passcodes for the recordings can be accessed via the CoursePlus > Online Library > Session

    • Screen sharing during class is recommended for effective troubleshooting.

Course Materials and Communications#

Course Team Contacts#

Labs Schedule#

Labs/Office Hours are scheduled from Monday to Friday. These hours are an open forum for you to seek help with homework, lab exercises, or clarify any course material

Lab instructions/solutions:

Homework Assignments#

Assignments are a crucial part of the coursework, allowing you to apply what you’ve learned:

Datasets and Supplementary Materials#

The course utilizes various datasets and Stata do-files, which are integral for lab exercises and homework:

Additional Resources and Evaluation#

  • Stata Version Note: A specific note on Stata 13 usage and compatibility issues is available here.

  • Course Evaluation: Your honest feedback is not just appreciated; it’s a cornerstone of how this course evolves. Please don’t miss out on the chance to shape future iterations in week 8.

  • Stata-focused Discussion Community on GitHub: An opportunity to engage with a broader Hopkins community interested in Stata programming. Requires a GitHub account and a willingness to learn and share.

Weekly Insights: Updates, Announcements, and Solutions#

See also

  • Updated as of 05/16/2024 at 6:27PM

  • New!!!Week 7 supplement on handling string variables (e.g. drug names)

  • Week 8 Video has is now available

  • After the term ends, you’ll be invited to volunteer in a Stata study.

    • The study compares prompt-based programming (our novelty) with traditional syntax programming (what you’ve learned).

    • The goal is to test whether individuals with no prior knowledge of programming can use our novel programming tool to achieve faster and more accurate analytic outputs.

    • Each participant will serve as their own control.

    • There are no disclosure risks as we will not keep your names.

    • Since we’ll have submitted grades to the Academic Registrar, participation is truly voluntary.

    • Help benefit the next generation of students by participating!

Operating system ambiguity#

hist age
sum age
regress age 
if c(os) == "MacOSX" { //forward slash
    graph export wk7output/age_tx.png, replace
}
else { //backslash
	graph export wk7output\age_tx.png, replace
}
ls  wk7output

Rubric for all Your Homeworks#

Total Points: 100 (no student will lose more than 20 points; some students may score as many as 105 points)#

  1. Code Structure and Documentation

    • Clarity (up to -1 point): Deduct if code is disorganized or hard to follow. (deduct only once, even if several blocks of code are disorganized; use same approach for other issues)

    • Comments (up to -2 points): Deduct for inadequate comments explaining code blocks, methods, or variables.

    • Readability (up to -1 point): Deduct for poor use of spacing, indentation, or naming conventions.

  2. Execution and Functionality

    • Correctness (up to -3 points): Deduct if the script has errors or does not produce the expected output.

    • Efficiency (up to -2 points): Deduct if code is inefficient with unnecessary repetitions or calculations.

      • Based on this point, even the solution we will later share with you isn't perfect; there's about -1 points for repetition in creating the .xlsx file

      • No body is perfect

      • Yet redeeming qualities could see you earn bonus points!

    • Completeness (up to -1 point): Deduct if any part of the script is missing or non-functional.

  3. Data Handling and Analysis

    • Data Import (up to -1 point): Deduct if data is incorrectly imported or cleaned.

    • Variable Creation (up to -2 points): Deduct if variable creation or transformation is incorrect.

    • Descriptive Statistics (up to -2 points): Deduct if there are errors in the computation or display of statistics.

  4. Results and Output Quality

    • Table Outputs (up to -2 points): Deduct if outputs in both .log and .xlsx formats are incorrect or unclear.

    • Labeling and Presentation (up to -2 points): Deduct if variables and results are improperly labeled or presented.

    • Interpretation (up to -1 point): Deduct if the results are not briefly explained or interpreted correctly.

Additional Considerations:#

  • Innovation (Bonus up to +5 points): Award for creative approaches that enhance the analysis or presentation. Perhaps five innovations earn five points. Three earn three, etc.

Acknowledgements#

The foundational pillars of this Spring term course were laid by my esteemed mentors and colleagues in the Department of Surgery at Hopkins, Dorry Segev and Allan Massie. Their move to NYU left big shoes to fill, and it has been an absolute honor to continue their legacy.

Having co-taught the summer institute alongside Allan Massie for three years, I’ve been uniquely prepared for this role, thanks to his exceptional mentorship. The core content of this class, akin to the enduring tune of a folk song, was originally composed by Allan. With time, we’ve reinterpreted this melody, introducing new harmonies and dissonances to accommodate the evolution of technology and challenges, including the introduction of this Jupyter Book hosted on GitHub, powered by Python, and navigated with the assistance of GPT-4. My foray into computer programming has been unexpected yet rewarding.

Vincent, your Analytic Mentor, exemplifies the journey from student to teacher, having advanced from being a student in this course to a lead TA, and now, a valued colleague and collaborator in the Department of Surgery. His progression is a testament to the potential that lies within each of you. This course is just the beginning. Like Vincent, many of you have the potential to transition from learners to leaders in the field. Engage deeply, and let’s see where this journey takes you

Our teaching assistants are nothing short of remarkable. Their dedication is evident, whether returning from previous years or joining as exceptional doctoral candidates from the Department of Epidemiology, drawn to this course by their passion for teaching. A special note for our MD/MPH students: one of our TAs has seamlessly bridged their MD/MPH experience at the Bloomberg School into two straight years of teaching, enhancing our team’s cohesion and your learning experience. She’s heading off to residency after this May!

Finally, the positive evolution of this course has been significantly shaped by feedback from over 300 students across the last two years. This feedback, including detailed critiques from a particularly insightful student, has been instrumental in refining our educational approach, aiming to balance challenge with skill to foster an engaging and productive learning environment.

()