STA 9750 - Course Syllabus

All syllabus and course schedule provisions subject to change with suitable advance notice.

Instructor

Course Meetings

Lecture / Lab Sessions

  • Virtual (Synchronous Online)
  • Thursdays 6:05pm-9:00pm
    • Zoom link provided via Brightspace

Office Hours

  • In-Person
  • Virtual:
    • Thursdays 4:30pm-5:30pm
    • Zoom link provided via Brightspace

Grading

  • 24% Weekly Pre-Assignments
    • 3% Each (best eight of ten: lowest two dropped)
    • Submission via Brightspace
  • 36% Mini Projects
    • 7% Each (four total)
    • 2% per Peer Feedback (Meta-Review)
  • 40% Course Project
    • 5% Proposal
    • 5% Mid-term Check-In
    • 10% Final Presentation
    • 7.5% Final Report
    • 12.5% Individual Evaluation

Opportunities for Extra Credit: Several opportunities for extra credit will be made available. These include:

  1. Participation in Course Discussion Board (Piazza)

  2. Correction of errors in published course materials (via Github pull request)

  3. Contributions to and enhancements of the course pre-assignments and in-class activities. These should be submitted as a Github pull request against the Course Repository.

    Note that contributions to course materials don’t have to be large to be valuable. You can simply clarify points that were not obvious to you, add new auto-graded exercises (see existing materials for examples), create new labs, or even add whole new topics.

    You can take part in this extra credit even if you are very new to R: if anything, being a recent learner helps you approach this topic with new eyes and to identify un-clear or “dangerous” edges.

    Obviously, you should try to make your contributions as accurate as possible, but let yourself be paralyzed by concern: the instructor will review any contributions before making anything “official.”

Final course grades will be curved in accordance with relevant program, departmental, school, and college policies.1

Regrading Policy

If you feel an assignment has been improperly graded, please contact the instructor by private message on the course discussion board within 48 hours of the graded assignment being returned. Note that the instructor will regrade the assignment de novo, so your grade may be adjusted upwards or downwards.

Late Work Policy

This course has many moving pieces and late work will not be accepted. Specifically, late submission slows down the peer review cycle used in thise course. All assignments can be submitted multiple times on Brightspace, so you are strongly encouraged to submit early and to submit often. Students are also encouraged to load the course deadlines file into their personal calendar to better track key course dates.

Specifically, technology problems will not be accepted as an excuse for late work.

Individual exceptions will be made i) with prior written approval of instructor; or ii) ex post with written note from the Office of the Dean of Students.

Tentative Course Schedule

Week Lecture Date Topics Pre-Assignment In-Class Lab Mini-Projects Additional Notes
1 2025-01-30 Course Overview and Key Infrastructure:
  1. R and RStudio
  2. git and GitHub
 None Lab #01 Mini-Project #00 assigned. Due Wednesday February 12, 2025 at 11:45pm ET
2 2025-02-06
  1. Introduction to Reproducible Research:
    • Markdown and Quarto
    • GitHub Pages
  2. Getting Help
  3. Interactive Use of R
    1. Use of the REPL
    2. Installing and Loading Packages
 PA #02 Lab #02
3 2025-02-13 Basic Data and Control Structures in R:
  1. Vectors
  2. Subsetting
  3. Control Flow
  4. Function Calls
  5. Data Frames
Introduction to Course Project		 PA #03 Lab #03 Mini-Project #01 assigned. Due Wednesday February 26, 2025 at 11:45pm ET Peer Feedback on Mini Project #00 due Wednesday February 19, 2025 at 11:45pm ET
4 2025-02-20 Tidy Data Manipulation I:
  1. Single Table Operations
  2. Factors
  3. Dates & Times
Review of git fundamentals.		 PA #04 Lab #04
5 2025-02-27 Tidy Data Manipulation II:
  1. Multi-Table Operations (Joins)
  2. Pivots
  3. Working with Missing Data
Debugging Techniques		 PA #05 Lab #05 Mini-Project #02 assigned. Due Wednesday March 26, 2025 at 11:45pm ET Peer Feedback on Mini Project #01 due Wednesday March 05, 2025 at 06:45pm ET
2025-03-06 No class: Baruch on Wednesday Schedule (President’s Day Make-Up)
6 2025-03-13 In-Class Project Proposal Presentations
Optional Enrichment Topic: SQL
7 2025-03-20 Plotting I:
  • Introduction to ggplot2
  • Customizing Formatting
  • Maps
 PA #07 Lab #07
8 2025-03-27 Tools for Interactive Data Analysis
  • shiny
  • ggplot2 Extensions for Animated and Interactive Plots
 PA #08 Lab #08 Mini-Project #03 assigned. Due Wednesday April 23, 2025 at 11:45pm ET Peer Feedback on Mini Project #02 due Wednesday April 02, 2025 at 11:45pm ET
9 2025-04-03 Data Import PA #09 Lab #09
10 2025-04-10 Mid-Semester Check-In Presentations
Optional Enrichment Topic: Functional Programming Tools
2025-04-17 No class: Baruch Spring Recess
11 2025-04-24 Elements of Web Scraping
  • Introduction to HTTP and HTML
  • httr2
  • rvest
  • JSON
 PA #11 Lab #11 Mini-Project #04 assigned. Due Wednesday May 07, 2025 at 11:45pm ET Peer Feedback on Mini Project #03 due Wednesday April 30, 2025 at 11:45pm ET
12 2025-05-01
  • Strings & Regular Expressions
  • Classical Statistical Modeling in R
 PA #12 Lab #12
13 2025-05-08 Predictive Modeling in R PA #13 Lab #13 Peer Feedback on Mini Project #04 due Wednesday May 14, 2025 at 11:45pm ET
14 2025-05-15 Final Presentations
Course Wrap-Up

Workload Expectations

The following approximate breakdown of expected course workload is intended to help you properly prepare for and schedule the out-of-class work associated with this course. Note that, persuant to relevant Federal and State regulations, a 3-credit course taken over a 15 week semester should require approximately 6 hours of out-of-class work from an average student, or 90 hours total over the course of the semester.23 This time should be roughly allocated as:

  • Weekly Pre-Assignments (10 hours)
  • Review of Previous Week’s Materials (10 hours)
  • Mini-Projects: (35 hours total)
    • Mini-Project #00 (3 hours):
      • Initial Submission (2 hours)
      • Peer Feedback (1 hour)
    • Mini-Projects #01-#04 (32 hours total; average 8 hours per mini-project)
      • Initial Submission (28 hours total; 9 hours for Mini-Projects #01 and #02
        • 5 hours for Mini-Projects 03 and 04)
      • Peer Feedback (4 hours total; 1 hour per mini-project)
  • Course Project (35 hours)
    • Proposal Presentation (3 hours)
    • Check-In Presentation (3 hours)
    • Final Presentation (5 hours)
    • Individual Report (20 hours)
    • Group Report (4 hours)

Note that, for the course project, the individual and group reports will require work throughout the semester, not simply at the end, though the majority of the effort is likely required in the latter half of the semester. As such, the first two Mini-Projects are designed to take more effort than the final two.

All syllabus and course schedule provisions subject to change with suitable advance notice.

Footnotes

  1. Theoretically, this may result in scores equivalent to an A in an un-curved course receiving a lower grade in this course. In practice, the instructor will design course assessments to induce a range of scores and does not anticipate “down-curving” happening.↩︎

  2. For this course, an average student is a student who enters the course with:

    1. Basic computer literacy, including use of the file system, plain text files and editors, etc.;
    2. A small amount of programming experience, not necessarily in R; and
    3. Fluency with statistics and data analysis at the level of (at least) STA 9708, ideally STA 9700;

    and is earning a B-range grade. If you have less background or are aiming for a higher grade, you should expect to commit proportionally more time to this course.↩︎

  3. The CUNY Graduate Center has a useful summary of these expectations. Baruch courses follow the same standards.↩︎