Unit 5 · Data · Lesson 5.1

Data Everywhere

Welcome back. This unit is about data — and the only new syntax is one way to read a file. Everything else is Unit 3’s lists, loops, and procedures, pointed at bigger inputs. Today: what a dataset actually is, and who decides its shape.

Big Idea 2 — DataCED 2.3 · Unit 5 begins

Before this lesson — read on Runestone

18.1 Web Data

Skim it for one idea: data on the web is just text a program can read. Enrolled in our Runestone course? Open it from there so your progress counts.

Where We Are

People→ Bits→ Programs→ Internet→ Security→ Big Data→ Impact

Big Data lights up for the first time. Unit 1 was the first half of Big Idea 2 — how bits represent things. This unit is the second half: what you can learn from a pile of them, and what you can’t.

The Hook

What did your phone record about you in the last hour? Write a list. Be specific. Take two minutes.

Now compare it to this: location every few minutes, step count, screen time per app, every notification and whether you opened it, which Wi-Fi networks were nearby, battery level, and how long the screen stayed on. Most lists are badly short. Every one of those is a field someone decided to record.

The Idea

A dataset is a collection of data organized so a program can work with it. Most datasets you’ll meet are tables:

Each row is a record — one thing being described (one student, one book, one trip).
Each column is a field — one kind of information recorded for every record.

Data that fits neatly into rows and columns is structured data. Photos, voicemails, and free-text comments are unstructured — before a program can count anything in them, a person has to decide what to pull out.

A dataset is bits with a shape someone chose. What a row means, which columns exist, what a blank cell means, and which categories are allowed — all of it is a human decision, and every one of those decisions changes what the data can tell you.

You already have the tools. A dataset is a list of records. You’ve traversed lists since 3.8, counted and summed since 3.4, and written procedures since 3.12. This unit points those exact skills at real data. Nothing you learned before break is wasted.

Try It — Build a Dataset From the Room

Five students answered three questions: grade, club, and minutes to get to school. Click any cell to see what it is. Then look hard at the rows — the design problems are already here.

Click a cell.

Three problems you just hit

In class

Build your own 10-row, 4-column dataset from the room. Decide your columns before you collect. Write down every decision you had to make that you didn’t expect.

Vocabulary

data Big Data: Values that describe something — numbers, text, dates, locations — stored as bits.
dataset Big Data: A collection of data organized so it can be processed, usually as rows and columns.
record Big Data: One row of a dataset: everything recorded about one thing (one student, one trip).
field Big Data: One column of a dataset: one kind of information recorded for every record.
structured data Big Data: Data organized into a fixed format, like a table, so a program can process it directly.

Check

Five questions. Records are rows; fields are columns.

0 of 5 answered

Impact Check

The fields you never see

The list from the hook is only what your phone shows you. Apps you install can collect more, and each one decides its own fields. In 5.2 you’ll see that some of the most revealing data isn’t the content at all — it’s the data about the content.

Connections

Came from Unit 1 — everything is bits. A dataset is bits arranged into records and fields.

Came from 3.6–3.10 — a dataset is a list. Traverse, count, accumulate — you already know how.

Next in 5.2 — metadata: the data about your data, and why it’s often more revealing than the data itself.

Returns in 5.10 — whatever a dataset’s designers chose not to collect, it can never tell you.