DANL 101 · Fall 2026 · SUNY Geneseo

Learn to think with data.

Analyze data with R, create clear visualizations, and tell evidence-based stories that help people decide—with generative AI supporting the process.

Start with Week 9

Everything in reach

Course materials

Find the available lecture slides, classwork, and homework below. Weekly pages show how the materials fit together.

Lecture slides

Classwork

View all classwork

Homework

Exams

Group projectBrowse project topics, supporting datasets, and the storytelling guideline. Semester mapSee the week-by-week course path, topics, and major dates. SyllabusCourse policies, outcomes, assessment, and full details.

Dates to keep in view

Plan around the moments that shape the semester.

First course meeting
Fall break
Midterm exam
Group presentations
Semester map

From analytics thinking to data-informed decisions.

Prepare data and study databases before the October 19 midterm, then explore visualization for three weeks and generative AI for two weeks before project work and presentations.

01Frame · Weeks 1–2Analytics questions + decision toolsTurn decisions into answerable questions, evaluate evidence, and use dashboards and analytics tools thoughtfully.→ 02Build · Weeks 3–4R foundations + statisticsWork with objects, vectors, data frames, and functions, then summarize center, spread, and distributions.→ 03Prepare · Weeks 5–8Statistics, transformation + databasesSummarize data, practice dplyr, and use Lecture 6 to understand data types, relational tables, keys, and left_join().→ 04Assess + visualize · Weeks 9–11Midterm + data visualizationAfter the October 19 midterm, begin Lecture 7 and Classwork 10 with histograms, then build toward group comparisons, relationships, and time trends.→
05Explore AI · Weeks 12–13 · Coming soonGenerative AI + responsible useStudy capabilities and limits, practice prompting and iteration, and verify outputs while considering disclosure, bias, and privacy.
06Apply + present · Weeks 14–16 · Coming soonProject studio + presentationsCheck the group project’s data and code, refine findings, rehearse, and present the completed analysis.
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