This site collects schedules, slides, assignments, setup instructions, syllabi, and project requirements for 36-613 Data Visualization and 36-614 Data Engineering and Distributed Environments.
Data Visualization runs August 24–October 9. Fall break is October 12–16. Data Engineering runs October 19–December 4, with presentations during finals week.
Mini 1 · Data VisualizationAug 24–Oct 9
Fall breakOct 12–16
Mini 2 · Data EngineeringOct 19–Dec 4
FinalsDec 7–12
Courses
Two separate seven-week minis
Both courses use real public data and emphasize reproducibility. Their final projects remain separate: one visualization project and two engineering projects.
36-613 · Mini 1
Data Visualization
Use NOAA Storm Events for shared visual practice, then advise an executive-search client with a global data-and-AI compensation archive.
MeetingsMW 9:30–10:50 a.m. · BH 140A
ToolsR, ggplot2, Quarto
PracticeDistributions, relationships, maps, time, text, critique
FinishSelf-contained workforce-compensation HTML report
Both minis meet Mondays and Wednesdays, 9:30–10:50 a.m., in Baker Hall 140A. Course pages link directly to available slides, checkpoints, and assignments.
Mini 1 · Data Visualization
Aug 24–Oct 9 · R + Quarto + NOAA Storm Events
Foundations and categorical dataMonday meets remotely; Wednesday is an asynchronous recorded lecture
Categories to distributionsConditional views, histograms, ECDFs, density
Two quantitative variablesLabor Day Monday; trends, residuals, and overplotting Wednesday
High-dimensional dataMany variables, distance, MDS, and PCA
Embeddings, time, and spaceNonlinear embeddings, trends, time series, and spatial foundations
Maps, design, and textSpatial displays, high-quality graphics, narrative fields
Animation and TBDAnimation Monday; Wednesday lecture TBD; Critique 2 due Wed; final HTML report due Fri
Mini 2 · Data Engineering
Oct 19–Dec 4 · PostgreSQL + Python + Azure
Start asking the databaseSQL basics: filtering, sorting, aggregation, and honest denominators
Join tables and design the schemaJoins, grain, keys, normalization, and schema design
Compose and diagnose queriesCTEs, windows, indexes, and query plans
Make the system transferablePipelines, Python, and the formal Friday handoff
Search and operate inherited dataFull-text search and managed cloud operations
Find the real scaling boundaryDistributed data Monday; no class Wednesday
Package, test, and explainInstallable commands, acceptance testing, and final rehearsal
Projects
Three client briefs
The visualization final uses workforce compensation. The two engineering projects exchange distinct systems on November 13.
DV · CLIENT 01
Workforce compensation
Turn global data-and-AI salary records into a target-employer recruiting playbook for an executive-search steering committee.