Software Tools for Data Analysis
STA 9750
Michael Weylandt
Week 1 – 2026-09-01 (Tue) and 2026-09-03 (Thu)
Last Updated: 2026-09-12

STA 9750 Week 1

Today: Lecture #01: Course Overview and Key Infrastructure

These slides can be found online at:

https://michael-weylandt.com/STA9750/slides/slides01.html

In-class activities can be found at:

https://michael-weylandt.com/STA9750/labs/lab01.html

Upcoming TODO

Upcoming student responsibilities - Tuesday Section:

Date Time Student Responsibility
2026-09-08 6:00pm ET Pre-Assignment #02 Due
2026-09-14 11:59pm ET Syllabus Quiz / Verification of Enrollment
2026-09-15 6:00pm ET Pre-Assignment #03 Due
2026-09-22 6:00pm ET Pre-Assignment #04 Due
2026-09-24 11:59pm ET Team Roster Submission
2026-09-25 11:59pm ET Mini-Project #00 Due
2026-09-29 6:00pm ET Team Contract Due
2026-09-29 6:00pm ET Project Proposal Presentation Slides Due

Upcoming TODO

Upcoming student responsibilities - Thursday Section:

Date Time Student Responsibility
2026-09-10 6:00pm ET Pre-Assignment #02 Due
2026-09-14 11:59pm ET Syllabus Quiz / Verification of Enrollment
2026-09-17 6:00pm ET Pre-Assignment #03 Due
2026-09-24 6:00pm ET Pre-Assignment #04 Due
2026-09-24 11:59pm ET Team Roster Submission
2026-09-25 11:59pm ET Mini-Project #00 Due
2026-10-01 6:00pm ET Team Contract Due
2026-10-01 6:00pm ET Project Proposal Presentation Slides Due

Other Important Dates

Other Important Dates:

Date Instructor Action
2026-09-08 Mini-Project Released #00
2026-09-15 Course Project Description Finalized
2026-09-22 Mini-Project Released #01
2026-09-28 Mini-Project Peer Feedback Assigned #00

STA 9750 Week 1

Weekly Course Updates:

  • Brief updates and reminders about course logistics
  • Syllabus and Brightspace are binding
    • If something is left out of here, it still happens!

Today: Introduction to STA 9750

STA 9750: Software Tools for Data Analysis

Course Overview

STA 9750 is:

  • Establishing a professional data analyst portfolio
  • Increasing your effectiveness in other courses
  • Building your networks with faculty and classmates
  • Getting s#!t done

Learning Objectives

Formal learning objectives can be found here

Key aims:

  • Deal with real data, no matter how messy and unhelpful
  • Engage with important technologies and learn to quickly adopt new tools
  • Communicate to both technical and non-technical audiences
  • Perform substantial and thoughtful analyses

Course Website

All course information can be found on the course website

No authentication required - mobile friendly

Source code on my GitHub

Course Website

Look online for:

Instructor

Me: Michael Weylandt

Assistant (i.e., new-ish) Professor in the Department of Info. Systems and Statistics

Previously:

  • Researcher at Sandia National Labs (US Government Lab)
  • Postdoc with US Intelligence Community
  • Ph.D. in Statistics at Rice University (Houston, TX)
  • “Quant” at Morgan Stanley and a (defunct) hedge fund

See my website for more on me and my research

Course Schedule

Lecture / Lab sessions:

  • Tuesdays and Thursdays on Zoom (here!) at 6:05pm
  • 14 weeks. Holidays on Oct 13 (Tue) + Nov 26 (Thu)
  • No penalty for missing class.
    • Slides and in-class activities online
    • Lectures generally not posted

Course Schedule

Presentation days:

  • Student Presentations on Sep 29 (Tue) + Oct 01 (Thu) + Oct 22 (Thu) + Oct 27 (Tue) + Dec 01 (Tue) + Dec 03 (Thu)
  • First two can be one per team, so only required day is final presentation
  • Order will be randomly selected by instructor
    • I expect you to be able to attend all of class on presentation day

Getting Help

Online Office Hours:

  • Before class (on days we have class) at 5:00pm
  • Different Zoom Link

Asynchronous

  • Course discussion platform (MS Teams)

DataCamp

This semester, I am providing free access to DataCamp courses

  • 100% optional
  • Additional exercises and review of class material
  • Access through sign-up link on Brightspace

Restricted to @stu-mail.baruch.cuny.edu emails - send a private message via the discussion board if you have a different CUNY email and I’ll special-case you

Course Schedule

Major Topics:

  • Web Communication and Literate Programming Technologies - 2 weeks
  • Introduction to R - 1 week
  • Tidy Data Manipulation (SQL-type operations) - 2 weeks
  • Plotting and Data Visualization - 2 weeks
  • Data Import, Web Scraping, and Text Cleaning - 3 weeks
  • Statistical Analysis - 1 week

Grading

  • 24% Pre-Assignments
  • 30% Mini-Projects
  • 46% Course Project

Extra credit for contribution to course materials or ‘above and beyond’ on the Course Discussion Board

Final grades curved to match ZSB grading guidelines

Course Policies

See syllabus for fine print:

  • Regrading: Must request within 48 hours; total regrade of assignment
  • Late work: Not without prior permission or DoS letter ex post
  • Grace period: two days on mini-project initial submission, none otherwise
  • Grade Curve: One at the end of the semester
  • External Resources / AI: Free to use for coding only, not writing
  • Absences: No excuse required, but must attend on presentation days
  • Accommodations: ADA (SDS) or Religious (direct with instructor)

Pre-Assignments

Pre-Assignments:

  • Weekly reading before class followed by a short quiz
  • Introduce new material + helps me know where folks are confused
  • Starts next week
  • Every week except three presentation weeks

Pre-Assignments

Pre-Assignments:

  • 24% of Overall Grade
  • Mix of new material and review questions
  • Lowest 2 dropped (best 8 of 10)
  • Take as many times as needed to get 100% (30/30)
  • Brightspace configuration is fragile, so send a message via the Discussion Board if issues

Mini-Projects

Traditional homework assignments - “mini-projects”

  • Guided real-data analysis using course technologies
  • Increasingly ambitious over the semester
  • Practice real data analysis
  • Communication and coding
  • Professional portfolio
  • One ungraded ‘set-up’ (“MP#00”) + best 3 of 4 graded

More later in the course

Mini-Projects

Due Dates:

  • MP#00: 2026-09-25 at 11:59pm ET (24 days from today, 22 days from today)
  • MP#01: 2026-10-16 at 11:59pm ET (21 days)
  • MP#02: 2026-11-06 at 11:59pm ET (21 days)
  • MP#03: 2026-11-27 at 11:59pm ET (21 days)
  • MP#04: 2026-12-11 at 11:59pm ET (14 days)

These take time - “due date” \(\neq\) “do date”

Mini-Projects

Suggestions welcome - see Archives for previous semesters

Mini-Projects

100 points per (graded) MP:

  • 80 points for submission
  • 20 points for peer review

Important to learn to read and write code

  • Single-blind peer review process
  • Grade from instructor on quality of comments

You can’t do peer review if you don’t submit (auto 0)

Best 3 of 4 (total score out of 100) used

Mini-Projects

A mix of ‘warm-up’ activities + final deliverable analysis:

Each project is graded on an 8 part rubric:

  • Project Skeleton
  • Written Communication
  • Tables & Presentation
  • Data Visualization
  • Exploratory Data Analysis
  • Code Quality
  • Data Preparation
  • Analysis and Findings

For earlier projects, automatic 10/10 (if you do the bare minimum) on some elements

Course Project

In lieu of exams, semester-long group project

  • Three presentations + two reports
    • Project Proposal
    • Mid-Semester Check-In
    • Final Presentation
    • Individual Final Report
    • Group Final Report

More online

Course Project

46% of grade:

  • Blend of individual (20%) and group (26%) grades
  • Timeline moved up a bit to give more end of semester breathing room
  • Start looking for teammates: form team by Sep 24
    • Proposal presentation and team contract due
  • First two presentations are mainly about making sure project is well-posed
    • Analysis due in final presentation + individual reports

Workload Expectations

Per federal and state requirements, 9 hours weekly = 6 hours outside of class

  • 10 hours of Pre-Assignments
  • 5 hours of Post-Class Review
  • 45 hours of Mini-Projects (homework)
  • 30 hours of Course Project

Note that course project = sum of all mini-projects - be ambitious!

Care Resources

See Care Resources for Students for helpful resources:

  • Mental health support
  • Physical health / medical care
  • Food Security
  • Financial Security
  • Immigration Support

Any questions?

Syllabus Quiz

“Syllabus quiz”:

  • Due Mon Sep 14
  • Brightspace
  • Unlimited attempts
  • Unlocks all subsequent assignments (and Brightspace reminders) so do early
    • If you don’t do this early, you will get 0s on early pre-assignments.

Getting Started with R and RStudio

Lab 01

Open Lab #01 and follow the instructions to get started with R and RStudio


Random assignment to Zoom breakout rooms

I will visit rooms to provide support


Call everyone back at 8:30pm

Wrap-Up

Course Agenda

  • Communicating Results (quarto) ⬅️
  • R Basics
  • Data Manipulation in R
  • Data Visualization in R
  • Getting Data into R
  • Statistical Modeling in R

Course Introduction

  • All materials on course website
  • Review of course structure and key policies
  • More details about mini-projects and course project to follow

Getting Started with R

  • Install R, RStudio, git, and quarto
  • Run a basic bit of R code to confirm things work well

Next Week

Syllabus Quiz on Brightspace

Writing documents using Markdown and Quarto

  • First Pre-Assignment due 2026-09-10 at 6:00pm ET
    1. Online reading
    2. Submit quiz on Brightspace

Life Tip of the Week

Weekly feature: “Life Tip of the Week” - Getting the most out of your time here

  • Advice about Baruch, finances, law, etc.

Today’s Tip: Take Advantage of Office Hours

  • What: Time set aside by Faculty for “drop-in” student interactions
  • Why: Homework help, review, diving deeper, chit-chat, connections to other courses, career advice - anything you want!
  • Where/When: Before class on Zoom

Build relationships with professors before you ask for things!

Happy to just ‘hang out’ but I will prioritize course related questions

Musical Treat