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Lessons

    Standards in this Lesson

    Common Core Math Standards
    6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    CSTA Standards
    1B-AP-11

    Decompose (break down) problems into smaller, manageable subproblems to facilitate the program development process.

    3A-AP-16

    Design and iteratively develop computational artifacts for practical intent, personal expression, or to address a societal issue by using events to initiate instructions.

    3A-AP-17

    Decompose problems into smaller components through systematic analysis, using constructs such as procedures, modules, and/or objects.

    3A-AP-23

    Document design decisions using text, graphics, presentations, and/or demonstrations in the development of complex programs.

    3A-DA-11

    Create interactive data visualizations using software tools to help others better understand real-world phenomena.

    3B-AP-14

    Construct solutions to problems using student-created components, such as procedures, modules and/or objects.

    K-12CS Standards
    6-8.Data and Analysis.Inference and Models

    People transform, generalize, simplify, and present large data sets in different ways to influence how other people interpret and understand the underlying information. Examples include visualization, aggregation, rearrangement, and application of mathematical operations.

    9-12.Data and Analysis.Visualization and Transformation

    Data can be transformed to remove errors, highlight or expose relationships, and/or make it easier for computers to process.

    Alabama Standards
    AL.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Arkansas Standards
    AR.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    California Standards
    CA.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Colorado Standards
    CO.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Connecticut Standards
    CT.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Delaware Standards
    DE.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Delaware Standards
    GA.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Hawaii Standards
    HI.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Idaho Standards
    ID.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Illinois Standards
    IL.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Iowa Standards
    IA.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Kansas Standards
    KS.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Kentucky Standards
    KY.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Louisiana Standards
    LA.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Maine Standards
    ME.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Michigan Standards
    MI.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Maryland Standards
    MD.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Massachusetts Standards
    MA.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Mississippi Standards
    MS.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Missouri Standards
    MO.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Montana Standards
    MT.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    New Hampshire Standards
    NH.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Nevada Standards
    NV.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    New Jersey Standards
    NJ.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    New Mexico Standards
    NM.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    North Carolina Standards
    NC.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    North Dakota Standards
    ND.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    New York Standards
    NY.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Oklahoma Standards
    OK.6.D.1.3

    Create and analyze box and whisker plots observing how each segment contains one quarter of the data.

    OK.7.D.1.2

    Use reasoning with proportions to display and interpret data in circle graphs (pie charts) and histograms. Choose the appropriate data display and know how to create the display using a spreadsheet or other graphing technology.

    OK.8.DA.S.01

    Analyze multiple methods of representing data and choose the most appropriate method for representing data.

    OK.8.IC.SI.01

    Communicate and publish key ideas and details individually or collaboratively in a way that informs, persuades, and/or entertains using a variety of digital tools and media-rich resources. Describe and use safe, appropriate, and responsible practices (netiquette) when participating in online communities (e.g., discussion groups, blogs, social networking sites).

    OK.A1.D.1.1

    Describe a data set using data displays, describe and compare data sets using summary statistics, including measures of central tendency, location, and spread. Know how to use calculators, spreadsheets, or other appropriate technology to display data and calculate summary statistics.

    OK.L1.DA.CVT.01

    Use tools and techniques to locate, collect, and create visualizations of small- and largescale data sets (e.g., paper surveys and online data sets).

    OK.L1.IC.C.02

    Test and refine computational artifacts to reduce bias and equity deficits.

    OK.PA.A.2.2

    Identify, describe, and analyze linear relationships between two variables.

    OK.PA.D.1

    Display and interpret data in a variety of ways, including using scatterplots and approximate lines of best fit. Use line of best fit and average rate of change to make predictions and draw conclusions about data.

    OK.PA.D.1.1

    Describe the impact that inserting or deleting a data point has on the mean and the median of a data set. Know how to create data displays using a spreadsheet and use a calculator to examine this impact.

    Ohio Standards
    OH.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Oregon Standards
    6.DR.A.1

    Formulate and recognize statistical investigative questions as those that anticipate changes in descriptive data related to the question and account for it in the answers.

    6.DR.B

    Collect and Consider Data

    HS.DR.A.1

    Formulate multivariable statistical investigative questions and determine how data from samples can be collected and analyzed to provide an answer.

    Pennsylvania Standards
    PA.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Rhode Island Standards
    RI.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    South Dakota Standards
    SD.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Tennessee Standards
    TN.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Utah Standards
    UT.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Vermont Standards
    VT.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Washington Standards
    WA.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    West Virginia Standards
    WV.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Wisconsin Standards
    WI.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Wyoming Standards
    WY.6.SP.A.1

    Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers.

    Textbook Alignment

    IM Algebra 1
    IM.Alg1.3.10

    Fossils and Flags

    IM.Alg1.1.9

    Technological Graphing

    IM 8 Math™
    IM.8.6.9

    FIXME: Add title

    Practices in this Lesson

    K12CS
    P7

    Communicating About Computing

    Math
    MP.2

    Reason abstractly and quantitatively

    MP.5

    Use appropriate tools strategically

    Science and Engineering
    SEP.3

    Planning and Carrying Out Investigations

    Students practice making a variety of chart types and then begin to investigate a real world dataset, which they will continue to work with for the remainder of the course.

    Lesson Goals

    Students will be able to…​

    • Explain why they chose their dataset

    • Describe their dataset

    • Make subsets from their dataset

    Student-facing Lesson Goals

    • Let’s all choose an interesting dataset to investigate.

    Materials

    Preparation

    • Decide how much choice you’re ready to offer your students before you begin. Research shows that choice increases student engagement! But focusing the whole class on a single dataset is also an option.

      • Would focusing your students on a single dataset make this doable for you? Because you teach younger students who might need more scaffolding? Or because you are new to teaching data science and managing fewer moving parts would increase your confidence? We recommend focusing on Global Food Supply & Production.

      • Are you ready to jump straight into supporting your students in working on a wide range of topics of their choosing? We have a full dataset library!

      • Want to give students choice from a shorter curated list…​ to shorten the decision-making process, focus on topics related to curriculur goals, or just to have fewer options to manage during class? We’ve assembled descriptions of individual datasets here. For those looking for a precurated shorter list, we’ve starred a few of them for you.

      • If you have time, you may want to complete all of the lessons with everyone getting extra practice analyzing Global Food Supply & Production and then have your students choose a dataset to analyze for their culminating research papers!

    • All students (or pairs) should log into CPO and open the "Animals Starter File" they saved from the prior lesson. If they don’t have the file, they can open the Animals Starter File.
    Glossary
    categorical data

    data whose values are qualities that are not subject to the laws of arithmetic

    data science

    the science of collecting, organizing, and drawing general conclusions from data, with the help of computers

    dataset

    a collection of related information that is composed of separate elements, but can be manipulated as a unit by a computer

    quantitative data

    number values for which arithmetic makes sense

    random sample

    a subset of individuals chosen from a larger set, such that each individual has the same probability of being chosen

    statistical inference

    using information from a sample to draw conclusions about the larger population from which the sample was taken

    🔗Review 20 minutes

    Overview

    Students practice making lots of chart types, focusing specifically on the "Consider Data" step in the Data Cycle and how it can be used alongside Contracts to help go from questions to code.

    Launch

    Let’s get some practice isolating the Rows and Columns needed to answer various questions, and use our knowledge of Contracts to help turn those questions into working code!

    Complete Consider and Analyze.

    Be sure to review student answers.

    🔗Choosing a Dataset 30 minutes

    Overview

    Students select a dataset that interests them, and do some thinking about why it interests them, what questions they’d like to answer and what hypotheses they have. They’ll be analyzing this data for a long time, so it’s critical to ensure a high degree of buy-in before signing off on a student’s choice!

    If you are opting to focus your whole class on a single dataset, we recommend skipping to the Exploring Your Dataset section of this lesson and using the dataset provided there. (It focuses on global food supply and production through environmental / geographic / cultural lenses and the variables were carefully selected to make sure it lends itself well for all kinds of data displays and discussions. You can, of course, opt to choose any dataset you’d like, from our library or otherwise.)

    Launch

    Data Science: it’s all about YOU!

    What data matters to you? What questions do you care about? We live in a world filled with data, gathered about almost every subject you can imagine.

    • Climate sensors are gathering data on temperature, humidity, oxygen and more…​practically everywhere on the globe.

    • Census data tracks the number of different groups of people, as well as their education, income level, and more.

    • Companies like Facebook, Amazon, and Google gather massive amounts of data on the websites you visit, what you chat about online, what you purchase, etc.

    This data is used to set public policy, draw voting districts, approve drugs, calculate school funding, decide which advertisements you see, and more.

    • Where else do you see data being gathered?

    • What are some other ways data is used in the world around you?

    Below is a list of every dataset already provided to students, with a corresponding Starter File that instantly imports the (cleaned) data into Pyret. We suggest giving students a direct link to this page, which lists all of the relevant links found in the lesson plan.

    Students can also find their own dataset, and use this Blank Dataset Starter File for Bootstrap:Data Science. See this tutorial video for help importing your own data into Pyret.

    Investigate

    Have students choose a dataset that is interesting to them and save a copy of it in their programs!

    Looking for a shorter list? We’ve starred a few good beginner datasets.

    The Environment & Health

    Global Waste by Country 2019

    Dataset Starter File

    World Cities' Proximity to the Ocean

    Dataset Starter File

    Earthquakes

    Dataset Starter File

    Air Quality, Pollution Sources & Health in the U.S.

    Dataset Starter File

    Health by U.S. County

    Dataset Starter File

    COVID in the U.S. by County

    Dataset Starter File

    Arctic Sea Ice

    Dataset Starter File

    Politics

    Countries of the World

    Dataset Starter File

    Gerry Mandering

    Dataset Starter File

    Marijuana Laws & Arrests by State 2018

    Dataset Starter File

    LAPD Arrests 2010-2019

    Dataset Starter File

    NYPD Stop, Search & Frisk 2019

    Dataset Starter File

    Refugees 2018

    Dataset Starter File

    State Demographics

    Dataset Starter File

    U.S. Income

    Dataset Starter File

    U.S. Jobs

    Dataset Starter File

    U.S. Voter Turnout 2016

    Dataset Starter File

    Sports

    Esports Earnings

    Dataset Starter File

    MLB Hitting Stats

    Dataset Starter File

    NBA Players

    Dataset Starter File

    NFL Passing

    Dataset Starter File

    NFL Rushing

    Dataset Starter File

    Entertainment

    ★Movies

    Dataset Starter File

    IGN video game Reviews

    Dataset Starter File

    International Exhibition of Modern Art

    Dataset Starter File

    North American Pipe Organs

    Dataset Starter File

    Pokemon

    Dataset Starter File

    Music

    Dataset Starter File

    Education

    College Majors

    Dataset Starter File

    U.S. Colleges 2019-2020

    Dataset Starter File

    ★R.I. Schools

    Dataset Starter File

    Evolution of College Admissions in California

    Dataset Starter File

    Nutrition

    Soda, Coffee & Other Drinks

    Dataset Starter File

    Fast Food Nutrition

    Dataset Starter File

    Would you like to contribute a dataset of your own, or is there something you’d like to change about one of ours?

    Synthesize

    Have students share which datasets they chose, and why they are interesting or important to them. What questions did they come up with?

    For the rest of this course, you’ll be learning new programming and Data Science skills, practicing them with the Animals Dataset and then applying them to you own data.

    🔗Exploring Your Dataset Start Today…​ continue in Upcoming Lessons

    Overview

    Students apply what they’ve learned about describing and making subsets from the Animals Dataset to their own dataset. If your students will all be focusing on the same dataset, we recommend using Global Food Supply & Production.

    Launch

    By now you’ve already learned what to do when you approach a new dataset.

    • With the Animals Dataset, you first read the data itself, and wrote down your Notices and Wonders.

    • You described the columns in the Animals Dataset, identifying which were categorical and which were quantitative, and whether they were Numbers, Strings, Booleans, etc.

    • You took random samples of the dataset, to explore inference and probability.

    Now, you’re doing to do the same thing with your own dataset.

    Investigate

    • Look at the spreadsheet or table for your dataset. What do you Notice? What do you Wonder?

    • Complete My Dataset, making sure to include at least two questions that can be answered by your dataset and one that cannot.

    • Save a copy of your starter file. In the Definitions Area, use random-rows to define at least three tables of different sizes: tiny-sample, small-sample, and medium-sample.

    Today we will begin working on the Dataset Exploration, which will prepare students for writing their research papers. We will return to this in upcoming lessons. We are just going to work on the first section for now.

    • Make a copy of Dataset Exploration, and open the starter file for your dataset.

    • Complete the first set of questions in the exploration paper.

    • What are the categorical columns in your dataset? How are those values distributed?

    • Turn to Complete Data Cycle: Shape of My Dataset, and use the Data Cycle to generate pie and bar charts.

    • What do these charts tell you? Add the images of these charts - along with your interpretation! - to the "Making Displays" section of the exploration document.

    • Do these displays bring up any interesting questions? If so, add them to the end of the document.

    Synthesize

    Have students share their findings. Were any of them surprising?

    These materials were developed partly through support of the National Science Foundation, (awards 1042210, 1535276, 1648684, and 1738598). 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.