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Lessons

    Aligns to:

    (Also available in CODAP)

    Students learn about Categorical and Quantitative data, are introduced to Tables by way of the Animals Dataset, and consider what questions can and cannot be answered with available data.

    Lesson Goals

    Students will be able to…​

    • Explain the difference between Categorical and Quantitative data

    • Identify whether a variable in a dataset is Categorical or Quantitative

    • Identify the Header Row and Identifier Column of a Table

    Student-facing Lesson Goals

    • Let’s learn about data inside tables.

    Materials

    Supplemental Materials

    Preparation

    • You know your students best! You can use the sample Opening Questions we’ve provided, but we recommend changing or adding your own questions that are appropriate, relevant, and engaging for your students.

    • Decide how the first activity (opening questions) will be run: will questions be printed for each student, group of students, or posted around the room?

    • Make sure student computers can access Animals Spreadsheet and the Animals Starter File.

    • You may want to make copies of the Animals Table if you aren’t working with a printed workbook. If you are working with a printed workbook, the table is included in the back of the book.

    🔗Questions that Matter 20 minutes

    Overview

    Students look at opening questions, either at their desks or in a walk around the room. They select a question they are personally interested in, and think about the data required to answer that question. This process draws a direct line between answering questions they care about and the basics of data science.

    Launch

    • A collection of thought bubbles with question marksLook at the provided list of Opening Questions, and take one minute to select a question that grabs your attention. Arrange yourselves into groups based on the question you like, making sure that each group has between 2-5 people.

    • Have each person in the group quickly share their gut reaction: What do you think the answer is?

    • After sharing initial reactions, have each person share their reasoning.

    • Does everyone in your group agree about the answers to their question?

    Students are VERY likely to try and explain their reasoning as soon as they give their gut answers. This can taint the answers of other students in the group - emphasize that this is about exposing our "gut reaction" or bias.

    Investigate

    What information would you collect to answer the question you selected? Take 5 minutes to think about what information you would need to collect, to find the answer.

    Optional: Which Questions Can we Answer?

    Datasets are useful for answering questions, but they can’t answer all the questions that we will wonder about for a given topic. In this activity students will look at a small dataset about a cyclist’s training rides and think about how they could use the table to answer each question or why a question cannot be answered using the table.

    Which of you like to ride bikes? What data might you collect about bike rides? Open to What Questions Can You Answer with the Given Data? This page includes a small dataset about a cyclist’s training rides and a set of questions. The data can be used to answer some, but not all, of the questions. With your partner, read each question. If it can be answered with what we know, explain how you could use the table to answer it. If it can’t be answered using the table, explain why not.

    Common Misconceptions

    Students may lean towards questions about individuals, instead of questions about what’s true for a group of individuals who vary from one to another. For example, instead of wondering what movie gets the highest rating, they should ask what’s the typical rating for movies in a list, or how much those ratings tend to vary.

    Synthesize

    Data is any piece of information about a group of individual or things. In this classroom, we could collect data about student names, ages, favorite foods, and so on.

    For each group…​

    • What were your gut reactions?

    • Did the question wind up being too vague? What did you need to do to make it specific?

    • What data would you gather?

    • What, if anything, were you surprised about?

      • If we wanted to find out if small schools are better than big schools, for example, we might want to gather data on SAT scores, college acceptance, etc. Each of these is a variable in our dataset: any two schools we look at could vary by each of them.

      • We can’t survey every school in the world (or get data on every movie ever made, or every police action!) but we can analyze a sample of them, and try to infer something about all of them as a whole.

    These questions quickly turn into a discussion about data — how you assess it, how you interpret the results, and what you can infer from those results.

    The process of learning from data is called Data Science. Data science techniques are used by scientists, business people, politicians, sports analysts, and people from hundreds of other different fields to ask and answer questions about data.

    We’ll use a programming language to investigate these questions. Just like any human language, programming languages have their own vocabulary and grammar that you will need to learn. The language you’ll be learning for data science is called Pyret.

    🔗Meet the Animals! 25 minutes

    Overview

    Students explore the Animals Dataset, sharing observations and familiarizing themselves with the idiosyncrasies and patterns in the data. In the process, they learn about Categorical and Quantitative data.

    Notice and Wonder Pedagogy

    This pedagogy is a widely-used best practice in Math-Ed, and is used throughout this course.

    In the "Notice" phase, students are asked to crowd-source their observations. No observation is too small or too silly! Students may notice that the animals table has corners, or that it’s printed in black ink. But by listening to other students' observations, students may find themselves taking a closer look at the dataset to begin with.

    The "Wonder" phase involves students raising questions, but they must also explain the context for those questions. Sharon Hessney (moderator for the NYTimes excellent What’s Going On in This Graph? activity) sometimes calls this "what do you wonder…​and why?"

    TIP: when a student shares something they notice, encourage the rest of the class to think about the observation and come up with questions that it sparks! This encourages students to listen more closely to one another, while also adding peer-validation for the observation they make.

    These phases should be done in groups or as a whole class, with ample time given to both Notice and Wonder.

    Launch

    Open the Animals Spreadsheet in a browser tab, or turn to the Animals Table. If you are working with a printed workbook, the table is included in the back of the book.

    Investigate

    Notice and Wonder This table contains data from an animal shelter, listing animals that have been adopted. We’ll be analyzing this table as an example throughout the course, but you’ll be applying what you learn to a dataset you choose as well.

    • Turn to Questions and Column Descriptions. What do you Notice about this dataset? Write down your observations in the first column.

    • Sometimes, looking at data sparks questions. What do you Wonder about this dataset, and why? Write down your questions in the second column.

    • There’s a third column, called “Answered by Dataset” — circle "Yes" if your Wonder can be answered by the dataset or "No" if it can’t.

    Have students share back their Notices (statements) and Wonders (questions), and write them on the board. Ask the class if each Wonder can be answered by the data, making sure that they have a few questions that can be answered, and a few that can’t. Also ask if some of their Wonders are about a group as a whole, rather than just individuals.

    • If you look at the bottom of the Animals Spreadsheet, you’ll see that this document contains multiple sheets. One is called "pets" and the other is called "README". Which sheet are we looking at?

    • Each sheet contains a table. For our purposes, we only care about the animals table on the "pets" sheet.

    Any two animals in our dataset may have different ages, weights, etc. Each of these is called a variable in the dataset. Data Scientists work with two broad kinds of data: Categorical Data and Quantitative Data. Sometimes it can be tricky to figure out if data is categorical or quantitative, because it depends on how that data is being used!

    Categorical Data tells us “what kind?”. Quantitative Data tells us "how much?".

    Categorical Data is used to classify, not measure. The laws of arithmetic do not make sense when it comes to categorical data!

    • “Species” is a categorical variable, because we can ask questions like “which species does Mittens belong to?"

    • We couldn’t ask if “cat is more than lizard” and it doesn’t make sense to "find the average ZIP code” in a list of addresses, because ZIP codes identify locations, not amounts.

    • What are some other categorical variables you see in this table?

      • Name, Sex, and Fixed

    Quantitative Data is used to measure a quantity, or to compare two pieces of data to see which is less or more the other, and by how much.

    • "Pounds" is a quantitative variable, because we can talk about how much more one animal weighs more than another or ask what the average weight of animals in the shelter is.

    • If we want to ask “how much” or “which is most”, we’re talking about Quantitative Data.

    • What are some other quantitative variables in this table?

      • Age, Legs, Weight, and Time to Adoption

    Complete Categorical or Quantitative?. Be sure to discuss your answers with your partner or group!

    Synthesize

    When two sisters visit the same webpage, they see targeted advertisements for two different vehicles: one sister sees an ad for a Ferrari, while the other sees an ad for a used Ford pickup.

    • What kinds of quantitative data about the sisters could have determined which saw the sports car and which saw the pickup?

      • Possible answers: income, age, number of country music songs purchased…​

    • What kinds of categorical data about them could have determined which ad they saw?

      • Possible answers: job, city, favorite food…​

    Data Science is all about making educated guesses about an entire group (called the population) based on data about a subset of that group (called the sample). It’s important to remember that tables are only a sample of a larger population: this table describes some animals, but obviously it isn’t every animal in the world! Still, if we took the average age of the animals from this particular shelter, it might tell us something about the average age of animals from other shelters.

    🔗Meet Pyret! 10 minutes

    Overview

    Students open up the Pyret environment (code.pyret.org, or "CPO") and see the Animals Dataset reflected there.

    Launch

    Let’s take a look at our programming environment, and see what the Animals Dataset looks like there.

    • Pyret programming language logoOpen the Animals Starter File in a new tab. Click “Connect to Google Drive” to sign into your Google account. This will allow you to save Pyret files into your Google Drive.

    • Next, click the "File" menu and select "Save a Copy". This will save a copy of the file into your own account, so that you can make changes and retrieve them later.

    Investigate

    • Click "Run" to tell Pyret to read the code on the left-hand side.

    • On the right-hand side, type animals-table and hit the "Enter" or "Return" key.

    • A table of animals appears on the right-hand side of the screen.

    • Look on the left-hand side of the screen. Where is Pyret getting animals-table from?

    The first line on the left-hand side of the screen tells Pyret to use a provided file, which contains tools we’ll want to use for this course.

    use context shared-gdrive("Bootstrap-DataScience-…​")

    After that, we see a line of code that defines shelter-sheet to be a spreadsheet.

    shelter-sheet = load-spreadsheet("https://docs.google.com…​.")

    This table is loaded from Google Drive, so now Pyret can see the same spreadsheet you do. If you look carefully, you’ll notice that the address listed here is the same address as the spreadsheet we just looked at!

    What Happens when Pyret loads a spreadsheet?

    • When Pyret imports a sheet, it reads all the cells and builds something it calls a Table. The original spreadsheet is still out there, exactly as it used to be!

    • When making a table, Pyret checks all of the cells automatically, and holds us accountable for using clean and complete data so that all displays can be trusted. Unlike Pyret, spreadsheets allow us to make displays out of any collection of cells - even if those cells are blank, contain mismatched data, or contain data from multiple, unrelated tables - which can jeopardize our analysis!

    • Each time we sort, filter or change a Table, Pyret makes a new Table. This means that we can explore one transformation or another, or even multiple transformations together…​and can always go back as many steps as we want. This is a big improvement over working with spreadsheets, where our options are to choose between making "backup" copies of sheets at every step (which means any change to one of those sheets needs to be replicated by hand in all of the other sheets), or modifying the original sheet (which makes it really hard to undo mistakes).

    • Checking data by hand and the inability to undo mistakes are problematic for students, teachers, and Data Scientists alike! While the strictness of programming may outweigh the benefits for younger students, for students who are comfortable writing code, programming is a vastly more powerful and learner-friendly tool for Data Science!

    After that, we see the following code:

    # load the 'pets' sheet as a table called animals-table
animals-table = load-table: name, species, sex, age, fixed, legs, pounds, weeks
  source: pets-sheet.sheet-by-name("pets", true)
end

    The first line (starting with #) is called a Comment. Comments are notes for humans, which the computer ignores. The next line defines a new table called animals-table, (loaded from the shelter-sheet!) and defines column names: name, species, sex, age, fixed, legs, pounds and weeks. We could use any names we want for these columns, but it’s always a good idea to pick names that make sense!

    Even if your spreadsheet already has column headers, Pyret requires that you name them in the program itself.

    Every table is made of cells, which are arranged in a grid of rows and columns.

    • The first row and first column are special.

    • The first row is called the header row, which gives a unique name to each variable (or “column”) in the table.

    • The first column in the table is the identifier column, which contains a unique ID for each row. Often, this will be the name of each individual in the table, or an ID number.

    Below is an example of a table with one header row and two data rows:

    name species sex age fixed legs pounds weeks

    "Sasha"

    "cat"

    "female"

    1

    false

    4

    6.5

    3

    "Mittens"

    "cat"

    "female"

    2

    true

    4

    7.4

    1

    • What is being used for the identifier column in this dataset? How many variables are listed in the header row for the Animals Dataset? What are they called?

      • There are eight variables listed: name, species, sex, age, fixed, legs, pounds, and weeks. name is the identifier column.

    • Try changing the name of one of the columns, and click "Run". What happens when you look at the table?

      • The column name also gets changed on the table.

    • What happens if you remove a column from the list? Or add an extra one?

      • When I remove a column, I get this message: "Loaded worksheet has 8 columns, but 7 column names were given."

      • When I add a column, I get this message: "Loaded worksheet has 8 columns, but 9 column names were given."

    After the header, Pyret tables can have any number of data rows. Each data row has values for every column variable (nothing can be left empty!). A table can have any number of data rows, including zero, as in the table below:

    name species sex age fixed legs pounds weeks

    Pyret lets us use many different kinds of data. In the animals table, for example, there are Numbers (the number of legs each animal has), Strings (the species of the animal), and Booleans (whether it is true or false that an animal is fixed).

    Synthesize

    Once you know how to program, you can do a lot with datasets:

    • Data Scientists display tables as all kinds of charts and graphs. For example, we might want to make a pie chart showing how many animals of each species we have.

    • Sometimes they want to filter a table, showing only a few of the rows. For example we might only want to look at animals where species is equal to "dog".

    • Or perhaps we want to build a column! For example, there could be a vaccination for all cats under the age of 3, and we want to add a needs-vaccine column that says true for cats under the age of 3, and false for everyone else.

    In this course, you’ll learn how to do all three: Display, Filter, and Build.

    These materials were developed partly through support of the National Science Foundation, (awards 1042210, 1535276, 1648684, 1738598, 2031479, and 1501927). CCbadge Bootstrap by the Bootstrap Community is licensed under a Creative Commons 4.0 Unported License. This license does not grant permission to run training or professional development. Offering training or professional development with materials substantially derived from Bootstrap must be approved in writing by a Bootstrap Director. Permissions beyond the scope of this license, such as to run training, may be available by contacting contact@BootstrapWorld.org.