Software Tools for Data Analysis
STA 9750
Michael Weylandt
Week 1 – Thursday 2026-01-29
Last Updated: 2026-01-27

Today’s Logistics

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 (if any) can be found at:

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

Upcoming TODO

Upcoming student responsibilities:

Date	Time	Details
2026-02-05	6:00pm ET	Pre-Assignment #02 Due
2026-02-12	11:59pm ET	Syllabus Quiz / Verification of Enrollment
2026-02-12	NA	Classes Cancelled (Lincoln’s Birthday)
2026-02-19	6:00pm ET	Pre-Assignment #03 Due
2026-02-19	11:59pm ET	Team Roster Submission
2026-02-20	11:59pm ET	Mini-Project #00 Due
2026-02-26	6:00pm ET	Team Contract Due
2026-02-26	6:00pm ET	Project Proposal Presentation Slides Due

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:

• Syllabus
• Weekly Pre-Assignments
• Mini-Projects
• In-Class Activities
• Course Project
• Additional Policies and Resources

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:

• Thursdays on Zoom (here!) at 6:05pm
  • Rescheduled class on Tuesday on 2026-04-21 (“Conversion Day”)
• 14 weeks. Holidays on Feb 12 + Apr 02 + Apr 09
• No penalty for missing class.
  • Slides and in-class activities online
  • Lectures generally not posted

Course Schedule

Presentation days:

• Student Presentations on Feb 26 + Mar 26 + May 07
• 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

In-person Office Hours:

• Wednesdays at 5pm (on days we have class)

Asynchronous

• Piazza course discussion platform

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 @baruchmail.cuny.edu emails - Piazza message 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 Piazza

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 Piazza 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: 2026-02-20 at 11:59pm ET (22 days from today)
• MP#01: 2026-03-13 at 11:59pm ET (21 days)
• MP#02: 2026-04-03 at 11:59pm ET (21 days)
• MP#03: 2026-04-24 at 11:59pm ET (21 days)
• MP#04: 2026-05-15 at 11:59pm ET (21 days)

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

Mini-Projects

• MP#00: Course Set-Up
• MP#01: Assessing the Impact of SFFA on Campus Diversity One-Year Later
• MP#02: How Do You Do ‘You Do You’?
• MP#03: TBD
• MP#04: TBD

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 Feb 19
  • Proposal presentation and team contract due Feb 26
• 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 Thu Feb 12
• Brightspace
• Unlimited attempts
• Unlocks all subsequent assignments so do early

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

Mini-Project #00

You can now start thinking about Mini-Project #00

• Create an account on GitHub + GitHub student developer pack
• Setting up your own website using quarto (like my course site)
  • Future Mini-Projects submitted here
• ‘Road test’ to make sure everything is working correctly
• Register on course discussion board (Piazza)

Due 2026-02-20 at 11:59pm ET

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 2026-02-05 at 6:00pm ET
  1. Online reading
  2. Submit quiz on Brightspace

Life Tip of the Week

Weekly feature: “Life Tip of the Week”

• Advice about Baruch, finances, law, etc.

Getting the most out of your time here

Life Tip of the Week

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