K–12 Data Analysis Education

The data already knows
where they're at.
Do you?

Education staff bring invaluable experience and expertise to every student they support — and nothing replaces that. We partner with educators to add one more lens: the same data, seen from different points of view, where small insights can lead to earlier, more meaningful intervention.

// technique 01 · delta analysis
⚑ flagged here — weeks before any threshold
The direction, not the number, is the story. explore →
// technique 02 · community intelligence
📖 Bookstores & readingwest of the river · 74%
🏁 Motorsports eventseast of the river · 68%
⚾ Youth sports leagueseast of the river · 63%
📖 Bookstores & readingeast of the river · 27%
Serve the neighborhood you can't see from your front porch. explore →
// technique 03 · staff analytics
🔄
3rd teacher change this year · Gr 7 Math
Affected students: GPA Δ −0.41 vs. stable-teacher peers
📊
Newest teachers → highest-need classrooms
The inverse of what students need — visible only in staff data
🤝
New teachers: 7.4 referrals/student · Veterans: 3.1
Not worse classrooms — a support gap the data can see
Staff patterns predict outcomes before grades do. explore →
// technique 04 · data storytelling
The same insight, told so a school board can't unsee it. explore →
// try it — 10 seconds

Two Ways to Read the Same Student

Drag through the semester. The snapshot view is how most schools read data — a number checked against a threshold. The trajectory view is what this curriculum teaches. Watch which one speaks up first.

Maya R. Grade 8 · synthetic student · attendance rate by week
Snapshot view — what the report shows
98%
Above threshold · no flag

Chronic-absence flag fires below 85%. Until then, this view has nothing to say.

Trajectory view — what the delta shows Steady · no concern
Week 1 of 14
Drag the slider to move through Maya's semester.

This is the exact analysis you'll build in Module 02: Delta Analysis 101 →

// from the teaching dataset

What the Data Knew All Along

Three patterns engineered into the synthetic district — because they're the patterns hiding in real ones. Each is a full module: you'll find it yourself, in Python, from raw records.

// where to next

Explore DataInEd

The philosophy, the curriculum, the deliverables, and the ways we can work together — pick your door.

Δ
Philosophy
Our Approach
Four lenses for reading school data from the student's point of view — delta analysis, community intelligence, staff analytics, and data storytelling.
🎓
Curriculum
Video Modules
Dual-track modules teaching Python-based education data analysis — from how to think about data through measuring whether interventions actually worked.
📊
End Solutions
Portfolio
Tableau, Power BI, SMS alerts, Teams, Slack, AR glasses — the same insight delivered in every format, for every audience, in every moment.
🏘️
External Data
Community Intelligence
Most administrators don't live in the neighborhoods they serve. Market data — the kind businesses buy before opening a store — shows you the community you serve, not the one you live in.
🤝
Work Together
Services & Pricing
Workshops, live cohorts, and district engagements — partner with DataInEd to put these methods to work. Transparent pricing; every engagement starts with a conversation.
🧰
Start Free
Free Resources
The learning is free: the full synthetic dataset on GitHub, starter notebooks, module videos, and all seven sample reports. No paywall, no strings.
👩‍🏫
Staff Data
Staff Analytics
Teacher turnover, experience distribution, assignment patterns — staff data predicts student outcomes before grades do. Are you reading it?
✉️
Connect
Get in Touch
Educator, administrator, data analyst, or researcher — reach out about collaboration, feedback, or anything the curriculum sparked for you.
"Adults and students look at the same school from different sides. The data holds both views — and reading the student's view is a skill anyone can learn."