You Already Know This
Before we define anything, take a moment to think about your own classroom or school. How many of the following sound familiar?
- An attendance sheet filled out every morning
- A gradebook with scores from every quiz and assignment
- A behavior log tracking referrals and interventions
- A spreadsheet showing which students have completed required reading assessments
- An end-of-year survey asking students how they felt about the school year
All of these are data. You have been collecting, organizing, and (at least sometimes) analyzing data your entire career. The concepts in this week's topic are not foreign to you — they are already woven into your professional life. What this topic will do is give you a vocabulary and a framework for thinking about that work more precisely, and help you understand how computers have transformed what is possible when people work with data at scale.
Data vs. Information: An Important Distinction
In everyday conversation, the words data and information are often used interchangeably. In computer science, the distinction matters.
Data refers to raw, recorded facts — numbers, words, measurements, observations. Data on its own carries no inherent meaning. A column of numbers like 87, 92, 74, 63, 95 is data. So is a list of student names. So is a spreadsheet full of daily attendance records going back three years.
Information is what you get when data is placed in context and interpreted to answer a question. Those five numbers — 87, 92, 74, 63, 95 — become information the moment you learn they are quiz scores for a student named Marcus, listed in chronological order over a semester. Now you can ask: Is Marcus improving? Did something happen between the third and fourth quiz? Should you intervene?
Data is the raw material. Information is what you build from it once you have a question to answer.
This distinction has a practical consequence: data without a question is just storage. Schools collect enormous amounts of data. The data only becomes useful — only becomes information — when someone brings a question to it. That question is what drives everything else.
Questions Come First
A common misconception about working with data is that you start by gathering as much of it as possible and then see what emerges. Sometimes that works — we will talk later this week about techniques that do exactly that. But more often, the most productive approach starts with a question.
Consider the difference between these two starting points:
Starting with data: "We have three years of attendance records. Let's see what's in there."
Starting with a question: "Do students with more than 10 absences in the fall semester perform worse on spring standardized assessments? And does that relationship hold equally across grade levels?"
Both are valid starting points. But the second one immediately tells you what data you need (attendance records, standardized assessment scores, grade levels), what time period matters (fall semester absences, spring assessments), and what kind of comparison you are making (a relationship between two variables, examined across subgroups). A clear question shapes every decision that follows.
This is why the new Iowa Data and Analysis standards, from kindergarten all the way through high school, begin with the skill of posing a question that can be answered with data. It is not a warm-up exercise — it is the most important step.
What Counts as Data?
Data takes many forms, and part of becoming comfortable working with it is recognizing data in its many shapes. Here are some of the most common types you will encounter:
Numbers (Quantitative Data)
Measurements and counts fall here: test scores, temperatures, number of absences, height in centimeters, distance in miles, revenue in dollars. Quantitative data can be added, averaged, graphed, and compared mathematically. Most people's mental image of "data" is quantitative.
Categories (Qualitative or Categorical Data)
Not all data is numeric. Student names, grade levels, subject areas, responses to a survey question like "What is your favorite part of the school day?" — these are categorical. You can count how many fall into each category, but you cannot average a list of names. Knowing which type of data you have matters enormously for choosing how to analyze it.
Text
Free-response survey answers, emails, student essays, social media posts, news articles — all of this is data too. Text data is harder for computers to work with than structured numbers or categories, but analyzing it at scale is one of the things that has made modern computing genuinely transformative. When a school district analyzes thousands of open-ended parent survey responses to identify common themes, that is text data analysis.
Images, Audio, and Video
A photograph is data. So is a recording of a classroom discussion, a surveillance video feed, or an X-ray. These are among the fastest-growing categories of data in the world today, and they present unique challenges — both technical and ethical — that we will return to later in the week.
Sensor and Log Data
Every time you tap your phone screen, a record is made. Every time a student badge is scanned at a school entrance, a record is made. Modern systems generate enormous streams of this kind of data automatically, without any human filling in a form. Websites, apps, smart devices, and building systems all produce logs of their activity. This is data too, even though no human consciously created it.
Scale Changes Everything
Teachers have always worked with data. What has changed is scale.
A teacher with 25 students can know their gradebook intimately. A principal overseeing 600 students is already working at a scale where patterns are hard to spot without tools. A district with 40,000 students, a state with 500,000, a national research database with data on 10 million children — at these scales, no human can make sense of the data without computational help.
This is where computer science enters the picture. The tools and techniques of data and analysis — databases, query languages, visualization software, statistical algorithms, and eventually the data mining techniques we will explore at the end of this week — exist precisely because human beings need help organizing, searching, and finding patterns in data collections that are too large to hold in any one person's head.
The goal of this week is not to make you a data scientist. It is to give you enough understanding of these tools and concepts that you can have informed conversations about them — with your students, with your administrators, and with the broader world that increasingly runs on data.
A Preview of the Week Ahead
The remaining readings in this topic will build on what you have just read. In Reading 2, we will trace the full PPDAC cycle — the process by which a question gets turned into an answer using data — and walk through a concrete school-based example from start to finish. In Reading 3, we will look more closely at how different types of data call for different kinds of organization and storage, which will set the stage for Topics 5B and 5C on databases.
Then Topics 5B and 5C will dig into databases — the most common tool for organizing structured data at scale — and Topic 5d will explore data mining, a family of techniques for finding patterns in large data collections that go beyond simply asking questions you already know how to phrase.
By the end of the week, you will have a clear picture of why data and analysis has become central to computer science education, and why Iowa's updated standards place it so prominently across all grade bands.