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

Grading

  • 24% Weekly Pre-Assignments
    • 3% Each (best eight of ten: lowest two dropped)
    • Submission via Brightspace
  • 30% Mini Projects #01-#04 (Best 3 of 4)
    • 8% per submission
    • 2% per peer feedback (scored via meta-review)
  • 46% Course Project
    • 1% Team Contract
    • 5% Proposal
    • 5% Mid-term Check-In
    • 2.5% Midterm Peer Evaluation
    • 7.5% Final Presentation
    • 7.5% Group Summary Report
    • 12.5% Individual Technical Report
    • 5% Final Peer Evaluation

Points will be aggregated for a maximum class score of 1000, so e.g., each pre-assignment will have a max score of 30, while the final peer evaluation will have a max score of 50.

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

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

  1. Participation in Course Discussion Board

  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 avoid letting yourself be paralyzed by concern: the instructor will review any contributions before making anything “official.”

Extra credit will be awarded at the instructor’s sole discretion.

Regrading Policy

If you feel an assignment has been improperly graded, please contact the instructor using the appropriate Brightspace quiz (“Regrade Request Form”) 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. Requests for re-grades after 48 hours will not be honored outside of exceptional circumstances.

Late Work Policy

This course has many moving pieces and late work will not be accepted, resulting in a zero on the assignment.

Specifically, late submission slows down the peer review cycle used in this 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 for their section into their personal calendar to better track key course dates.

Please note that work must be submitted on Brightspace unless specifically otherwise stated in the written course description or approved in writing by the instructor at least 48 hours before the deadline. Email submissions are not allowed.2

Technology issues are not an approved excuse for late submissions; in particular, Brightspace issues are not an approved excuse for late submission of the Mini-Projects. Brightspace support is provided by BCTC and the BCTC Help Desk. The Help Desk is not staffed 24 hours per day, so you are encouraged to submit early and submit often to avoid issues. The course staff is able to provide additional support for personal computer issues, including git and RStudio during regularly-scheduled Office Hours, but BCTC should be your first point of contact.

Individual exceptions to the above policies will be made i) with prior written approval of instructor at least 48 hours before the deadline; or ii) ex post with written note from the Office of the Dean of Students. Please note that, in particular:

  • The instructor is not able to provide ex post exceptions; these must be handled through DoS’s Notice of Absence procedures.
  • Verbal exceptions discussed in Office Hours or after class are not sufficient. You must follow-up on any discussions and get my written pre-approval by email or on the course discussion board.

For multi-part assignments such as the Mini-Projects, students who fail to submit the initial part of the assignment will not be able to participate in later parts of the assignment. In particular, students who do not submit their Mini-Project submissions before peer feedback assignments are made (after the two-day grace period) will not be assigned to perform peer feedback and no other students will be asked to provide comments on their submission.

Course Schedule

The tentative course schedule follows below. Any changes will be communicated verbally in class and by Brightspace announcement well in advance.

Note that class sessions are divided into:

  • Regular Lectures (11x)
  • Student Presentation Days (3x - Weeks 4, 8, and 13)

Labs and pre-assignments are numbered by lecture number (#01 to #11) while slides are numbered by week (#01 to #14).

Week Lecture Date Topics Pre-Assignment In-Class Lab Slides Mini-Projects Peer Feedback Additional Notes
1 2026-09-01 Lecture #01: Course Overview and Key Infrastructure
  1. R and RStudio
  2. git and GitHub
 None Lab #01 Slides Week #01

2

2026-09-08

Introduction to Course Project

Lecture #02: Tools for Reproducible Research

  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

Pre-Assignment #02 due at 6:00pm before class

Lab #02

Slides Week #02

Mini-Project #00 assigned: due Friday September 25, 2026 at 11:59pm ET.

Syllabus Quiz / Verification of Enrollment due on 2026-09-14

3

2026-09-15

Special Presentation: Library Data Science Resources

Lecture #03: Coding in R: Variables, Control Flow, Packages, Function Calls

  1. Vectors
  2. Subsetting
  3. Control Flow
  4. Function Calls
  5. Data Frames

Pre-Assignment #03 due at 6:00pm before class

Lab #03

Slides Week #03

4

2026-09-22

Lecture #04: Single Table dplyr Verbs

  1. Single Table Operations
  2. Factors
  3. Dates & Times

Review of git fundamentals.

Pre-Assignment #04 due at 6:00pm before class

Lab #04

Slides Week #04

Mini-Project #00 due Friday September 25, 2026 at 11:59pm ET.

Mini-Project #01 assigned: due Friday October 16, 2026 at 11:59pm ET.

Project Team Rosters due 2026-09-24 (Thursday) at 11:59pm ET

Professor will help coordinate teams for ‘unmatched’ students at the end of class
5 2026-09-29 In-Class Project Proposal Presentations
Optional Enrichment Topic: Additional R Basics 		Slides Week #05 Peer Feedback on Mini-Project #00 assigned on 2026-09-28; due Monday October 05, 2026 at 11:59pm ET. Team Contract and Slides due at 6:00pm
6 2026-10-06 Lecture #05: Multi-Table dplyr Verbs
  1. Multi-Table Operations (Joins)
  2. Pivots
  3. Working with Missing Data
Debugging Techniques		 Pre-Assignment #05 due at 6:00pm before class Lab #05 Slides Week #06 Peer Feedback on Mini-Project #00 due Monday October 05, 2026 at 11:59pm ET.
2026-10-13 No Class: Classes on Monday Schedule – Columbus Day Conversion Day
7 2026-10-20 Lecture #06: Plotting with ggplot2
  • Introduction to ggplot2
  • Customizing Formatting
 Pre-Assignment #06 due at 6:00pm before class Lab #06 Slides Week #07 Mini-Project #02 assigned: due Friday November 06, 2026 at 11:59pm ET. Peer Feedback on Mini-Project #01 assigned on 2026-10-19; due Monday October 26, 2026 at 11:59pm ET.

8

2026-10-27

Mid-Semester Check-In Presentations
Optional Enrichment Topic: SQL

Slides Week #08

Peer Feedback on Mini-Project #01 due Monday October 26, 2026 at 11:59pm ET.

Slides due at 6:00pm

9

2026-11-03

Lecture #07: Advanced ggplot2 – maps, interactivity, animation

Pre-Assignment #07 due at 6:00pm before class

Lab #07

Slides Week #09

Mini-Project #02 due Friday November 06, 2026 at 11:59pm ET.

Mid-Semester Peer Evaluations due at 11:59pm
10 2026-11-10 Lecture #08: Data Import and File System Usage; APIs
  • Maps
  • shiny
  • ggplot2 Extensions for Animated and Interactive Plots
 Pre-Assignment #08 due at 6:00pm before class Lab #08 Slides Week #10 Mini-Project #03 assigned: due Friday November 27, 2026 at 11:59pm ET. Peer Feedback on Mini-Project #02 assigned on 2026-11-09; due Monday November 16, 2026 at 11:59pm ET.
11 2026-11-17 Lecture #09: Introduction to Web Technologies & Web Scraping
  • Introduction to HTTP and HTML
  • httr2
  • rvest
  • JSON
 Pre-Assignment #09 due at 6:00pm before class Lab #09 Slides Week #11 Peer Feedback on Mini-Project #02 due Monday November 16, 2026 at 11:59pm ET.

12

2026-11-24

Lecture #10: Strings, Regular Expressions, and Text Processing

  • Strings & Regular Expressions
  • Classical Statistical Modeling in R

Pre-Assignment #10 due at 6:00pm before class

Lab #10

Slides Week #12

Mini-Project #03 due Friday November 27, 2026 at 11:59pm ET.

Mini-Project #04 assigned: due Friday December 11, 2026 at 11:59pm ET.
13 2026-12-01 Final Presentations
Optional Enrichment Topic: Functional Programming in R 		Slides Week #13 Peer Feedback on Mini-Project #03 assigned on 2026-11-30; due Monday December 07, 2026 at 11:59pm ET. Slides due at 6:00pm

14

2026-12-08

Course Wrap-Up

Lecture #11: Statistical Modeling in R

Pre-Assignment #11 due at 6:00pm before class

Lab #11

Slides Week #14

Mini-Project #04 due Friday December 11, 2026 at 11:59pm ET.

Peer Feedback on Mini-Project #03 due Monday December 07, 2026 at 11:59pm ET.
Finals Period

Group Report, Individual Report, Final Peer Evaluations due 2026-12-17

Peer Feedback on Mini-Project #04 due on Monday December 21, 2026 at 11:59pm ET

Final Grades posted to CUNYFirst by 2026-12-28

Week Lecture Date Topics Pre-Assignment In-Class Lab Slides Mini-Projects Peer Feedback Additional Notes
1 2026-09-03 Lecture #01: Course Overview and Key Infrastructure
  1. R and RStudio
  2. git and GitHub
 None Lab #01 Slides Week #01

2

2026-09-10

Introduction to Course Project

Lecture #02: Tools for Reproducible Research

  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

Pre-Assignment #02 due at 6:00pm before class

Lab #02

Slides Week #02

Mini-Project #00 assigned: due Friday September 25, 2026 at 11:59pm ET.

Syllabus Quiz / Verification of Enrollment due on 2026-09-14

3

2026-09-17

Special Presentation: Library Data Science Resources

Lecture #03: Coding in R: Variables, Control Flow, Packages, Function Calls

  1. Vectors
  2. Subsetting
  3. Control Flow
  4. Function Calls
  5. Data Frames

Pre-Assignment #03 due at 6:00pm before class

Lab #03

Slides Week #03

4

2026-09-24

Lecture #04: Single Table dplyr Verbs

  1. Single Table Operations
  2. Factors
  3. Dates & Times

Review of git fundamentals.

Pre-Assignment #04 due at 6:00pm before class

Lab #04

Slides Week #04

Mini-Project #00 due Friday September 25, 2026 at 11:59pm ET.

Mini-Project #01 assigned: due Friday October 16, 2026 at 11:59pm ET.

Project Team Rosters due 2026-09-24 (Thursday) at 11:59pm ET

Professor will help coordinate teams for ‘unmatched’ students at the end of class
5 2026-10-01 In-Class Project Proposal Presentations
Optional Enrichment Topic: Additional R Basics 		Slides Week #05 Peer Feedback on Mini-Project #00 assigned on 2026-09-28; due Monday October 05, 2026 at 11:59pm ET. Team Contract and Slides due at 6:00pm
6 2026-10-08 Lecture #05: Multi-Table dplyr Verbs
  1. Multi-Table Operations (Joins)
  2. Pivots
  3. Working with Missing Data
Debugging Techniques		 Pre-Assignment #05 due at 6:00pm before class Lab #05 Slides Week #06 Peer Feedback on Mini-Project #00 due Monday October 05, 2026 at 11:59pm ET.

7

2026-10-15

Lecture #06: Plotting with ggplot2

  • Introduction to ggplot2
  • Customizing Formatting

Pre-Assignment #06 due at 6:00pm before class

Lab #06

Slides Week #07

Mini-Project #01 due Friday October 16, 2026 at 11:59pm ET.

Mini-Project #02 assigned: due Friday November 06, 2026 at 11:59pm ET.
8 2026-10-22 Mid-Semester Check-In Presentations
Optional Enrichment Topic: SQL 		Slides Week #08 Peer Feedback on Mini-Project #01 assigned on 2026-10-19; due Monday October 26, 2026 at 11:59pm ET. Slides due at 6:00pm
9 2026-10-29 Lecture #07: Advanced ggplot2 – maps, interactivity, animation Pre-Assignment #07 due at 6:00pm before class Lab #07 Slides Week #09 Peer Feedback on Mini-Project #01 due Monday October 26, 2026 at 11:59pm ET. Mid-Semester Peer Evaluations due at 11:59pm

10

2026-11-05

Lecture #08: Data Import and File System Usage; APIs

  • Maps
  • shiny
  • ggplot2 Extensions for Animated and Interactive Plots

Pre-Assignment #08 due at 6:00pm before class

Lab #08

Slides Week #10

Mini-Project #02 due Friday November 06, 2026 at 11:59pm ET.

Mini-Project #03 assigned: due Friday November 27, 2026 at 11:59pm ET.
11 2026-11-12 Lecture #09: Introduction to Web Technologies & Web Scraping
  • Introduction to HTTP and HTML
  • httr2
  • rvest
  • JSON
 Pre-Assignment #09 due at 6:00pm before class Lab #09 Slides Week #11 Peer Feedback on Mini-Project #02 assigned on 2026-11-09; due Monday November 16, 2026 at 11:59pm ET.
12 2026-11-19 Lecture #10: Strings, Regular Expressions, and Text Processing
  • Strings & Regular Expressions
  • Classical Statistical Modeling in R
Pre-Assignment #10 due at 6:00pm before class Lab #10 Slides Week #12 Mini-Project #04 assigned: due Friday December 11, 2026 at 11:59pm ET. Peer Feedback on Mini-Project #02 due Monday November 16, 2026 at 11:59pm ET.
2026-11-26 No Class: Thanksgiving
13 2026-12-03 Final Presentations
Optional Enrichment Topic: Functional Programming in R 		Slides Week #13 Peer Feedback on Mini-Project #03 assigned on 2026-11-30; due Monday December 07, 2026 at 11:59pm ET. Slides due at 6:00pm

14

2026-12-10

Course Wrap-Up

Lecture #11: Statistical Modeling in R

Pre-Assignment #11 due at 6:00pm before class

Lab #11

Slides Week #14

Mini-Project #04 due Friday December 11, 2026 at 11:59pm ET.

Peer Feedback on Mini-Project #03 due Monday December 07, 2026 at 11:59pm ET.
Finals Period

Group Report, Individual Report, Final Peer Evaluations due 2026-12-17

Peer Feedback on Mini-Project #04 due on Monday December 21, 2026 at 11:59pm ET

Final Grades posted to CUNYFirst by 2026-12-28

Syllabus Quiz

After the first day of class, a syllabus quiz will be available on Brightspace. This quiz covers the key points of this syllabus and course policies. You must pass this quiz with 100% to be allowed to remain in this course. If you have not passed the syllabus quiz by the Verification of Enrollment deadline, you may receive a WN grade and be removed from this course.

Furthermore, completing the syllabus quiz is necessary to unlock all subsequent assignments in Brightspace. Note that, if you do not complete the syllabus quiz promptly, you will not be able to submit other assignments nor will you receive automated reminders about those assignments. Complete the syllabus quiz early to avoid getting a 0 on the early assignments in this course.

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.34 This time should be roughly allocated as:

  • Review of Lecture Materials (5 hours = 0.5 hours \(\times\) 10 lectures)
  • Weekly Pre-Assignments (10 hours = 1 hour \(\times\) 10 Pre-Assignments)
  • Mini-Projects: (45 hours total)
    • Mini-Project #00 (3 hours):
      • Initial Submission (2 hours)
      • Peer Feedback (1 hour)
    • Mini-Projects #01-#04 (42 hours total, assuming skipping one 3 MP)
      • Initial Submission (39 hours = 13 hours \(\times\) 3 MPs)
      • Peer Feedback (3 hours = 1 hour \(\times\) 3 MPs)
  • Course Project (30 hours)
    • Team Formation & Proposal Presentation (2 hours)
    • Check-In Presentation & Peer Evaluation (2 hours)
    • Final Presentation (3 hours)
    • Group Report & Peer Evaluation (3 hours)
    • Individual Report (20 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 final Mini-Project is designed to take less time than the prior three for students who have mastered the material up to that point.

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. Though ubiquitous, email is a remarkably ‘flaky’ service, providing the sender no way to guarantee their message arrives untampered and providing the recipient no way to guarantee the provenance of a message received. (This is not quite true: there are tools for more secure email but they are somewhat more difficult to use and are not supported at CUNY.) Brightspace is integrated with CUNY’s Identity Verification Services and allows students to guarantee correct submission. Note that Brightspace does not, by default, send students an email confirming submission, but I believe this is an option that can be enabled on the student’s end.↩︎

  3. 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.

    If you lack the prerequisite background listed above or simply wish to review it before the semester begins in earnest, please reach out to the instructor and I will be more than happy to provide supplementary readings.↩︎

  4. The CUNY Graduate Center has a useful summary of these expectations. Baruch courses follow the same standards. See also CUNY Central Policy.↩︎