STA 9750 - Pre-Assignments

In lieu of traditional homework, STA 9750 has weekly pre-assignments designed to achieve several interlocking goals:

  1. Provide initial exposure to that week’s topic before the lecture and lab session;
  2. Allow students with less previous programming experience more time to familiarize themselves with that week’s topic;
  3. Allow students to submit questions to be covered in class.

After completing the pre-assignment reading, understanding will be confirmed via a short quiz submitted via CUNY Brightspace. These are due just before class (Tuesdays or Thursdays at 6:00pm) and are short assignments, typically only a few questions, so extensions will not be given outside of exceptional circumstances.

These quizzes can be attempted as many teams as required on Brightspace, so it is expected that students will earn full credit. Don’t let these slip past you - these are the ‘easiest’ points you’ll get in this class and not getting them all can lead to a disappointing result at the end of the term.

For students desiring additional pre-class prep, an optional DataCamp course covering that week’s material will be made available. This is not required - and no credit will be given for completing it - but it may be helpful.

Pre-Assignments

Pre-Assignment for Week #01 / Lecture #01

None.

Pre-Assignment for Week #02 / Lecture #02 - Getting Started with Markdown

Due Dates:

  • Released to Students: September 01, 2026 (Tuesday) at 9:00pm ET
  • Due on Brightspace: September 10, 2026 (Thursday) at 6:00pm ET

In this Pre-Assignment, you will familiarize yourself with the basics of Markdown, an easy way to write and format documents. In class, we will use Markdown based tools to create dynamic data analysis documents seamlessly combining code, text, and graphics.

Pre-Assignment for Week #03 / Lecture #03 - Calculator Work with R

Due Dates:

  • Released to Students: September 08, 2026 (Tuesday) at 9:00pm ET
  • Due on Brightspace: September 17, 2026 (Thursday) at 6:00pm ET

In this week’s preassignment, you will familiarize yourself with some basic “calculator math” in R. You will also see how function calls work as we get ready to start some proper R programming.

Pre-Assignment for Week #04

None.

The September 22, 2026 (Tuesday) and September 24, 2026 (Thursday) class sessions will be dedicated to Course Project Proposals.

Pre-Assignment for Week #05 / Lecture #04 - Single-Table dplyr Verbs

Due Dates:

  • Released to Students: September 15, 2026 (Tuesday) at 9:00pm ET
  • Due on Brightspace: September 24, 2026 (Thursday) at 6:00pm ET

In this week’s preassignment, you will review dplyr’s “single-table” verbs. These are functions that take a single data frame and do something, typically returning another data frame. We can divide these into three major groups:

  • Subsetting rows (filter) and columns (select);
  • Changing and creating columns (mutate and less commonly, rename);
  • operating with group structure (group_by, summarize)

Pre-Assignment for Week #06 / Lecture #05 - Multi-Table dplyr Verbs

Due Dates:

  • Released to Students: September 22, 2026 (Tuesday) at 9:00pm ET
  • Due on Brightspace: October 08, 2026 (Thursday) at 6:00pm ET

In this week’s preassignment, you will review dplyr’s most important “multi-table” verbs, the join operators. These are functions that take multiple data frames and combine them together. You will need to use this type of functionality to combine data from different sources together in a principled and organized fashion. You will also learn a bit about the pivot_longer and pivot_wider functions used to change the shape of data frames. These are particularly useful in conjunction with joins: you will often need to reshape two tables to “join” properly (typically, lengthening them with pivot_longer) and then reshape them for downstream presentation (typically with pivot_wider).

Pre-Assignment for Week #07 / Lecture #06 - Lots of Plots

Due Dates:

  • Released to Students: October 06, 2026 (Tuesday) at 9:00pm ET
  • Due on Brightspace: October 20, 2026 (Tuesday) at 6:00pm ET

In this week’s preassignment, we begin to explore the wonderful world of statistical graphics.

Pre-Assignment for Week #08

None.

The October 27, 2026 (Tuesday) and October 22, 2026 (Thursday) class sessions will be dedicated to Course Project Mid-Semester Check-Ins.

Pre-Assignment for Week #09 / Lecture #07 - Taking Plots to the Next Level

Due Dates:

  • Released to Students: October 15, 2026 (Thursday) at 9:00pm ET
  • Due on Brightspace: October 29, 2026 (Thursday) at 6:00pm ET

In this week’s preassignment, we dive deeper into the world of statistical graphics, moving beyond simple static plots.

Pre-Assignment for Week #10 / Lecture #08 - Flat-File Data Ingest

Due Dates:

  • Released to Students: November 03, 2026 (Tuesday) at 9:00pm ET
  • Due on Brightspace: November 10, 2026 (Tuesday) at 6:00pm ET

In this week’s preassignment, we review the basics of reading data files into R.

Pre-Assignment for Week #11 / Lecture #09 - Intro to HTML

Due Dates:

  • Released to Students: November 05, 2026 (Thursday) at 9:00pm ET
  • Due on Brightspace: November 17, 2026 (Tuesday) at 6:00pm ET

In this week’s preassignment, students are introduced to the basics of CSS selectors.

Pre-Assignment for Week #12 / Lecture #10 - Strings and Things

Due Dates:

  • Released to Students: November 12, 2026 (Thursday) at 9:00pm ET
  • Due on Brightspace: November 24, 2026 (Tuesday) at 6:00pm ET

In this week’s pre-assignment, we begin to explore the world of text data. For the pre-assignment, we introduce some of the useful functions of the stringr package and dip our toes into the world of regular expressions. In class, we will apply these string-based tools to begin parsing data from web text.

Pre-Assignment for Week #13

None.

The December 01, 2026 (Tuesday) and December 03, 2026 (Thursday) class sessions will be dedicated to Course Project Final Presentations.

Pre-Assignment for Week #14 / Lecture #11 - Introduction to Statistical Modeling in R

Due Dates:

  • Released to Students: November 19, 2026 (Thursday) at 9:00pm ET
  • Due on Brightspace: December 10, 2026 (Thursday) at 6:00pm ET

In this week’s preassignment, we briefly introduce some of the basic statistical functionality of R.