Sorting Data

 ECT Lesson Plan: Sorting Data

Lesson plan at a glance...

Core subject(s)

Mathematics

Subject area(s)

Statistics & Probability

Suggested age

14 to 18 years old

Prerequisites

Familiarity with central tendency

Time

Preparation: 10 to 22 minutes

Instruction: 40 minutes

Standards

Core Subject: CCSS Math

CS: Australia, CSTA, UK

In this lesson plan…

Lesson Overview

Students gather data from classmates and analyze categorical and quantitative data using spreadsheet functions and/or Python. Decomposition is used to break the sorting process into individual steps and students learn how to design algorithms. Upon completion of this lesson, students will be able to write algorithms to calculate the mean, median, mode, and range of data. Additionally, students will be able to use spreadsheet functions or programming instructions to sort data based on different criteria, leading to the identification of patterns and trends.

For more advanced activities on sorting data, view the ECT Lesson Plan “Sorting the World’s Cities.”

Materials and Equipment

  • For the teacher:
  • Required: Presentation set-up
  • Internet-connected computer
  • Projector and projection screen or other flat projection surface
  • Required: Access to YouTube (http://www.youtube.com) for optional extension activity
  • Recommended: Whiteboard and markers or equivalent
  • For the student:
  • Required: Software Development Environment

Preparation Tasks

Confirm that your computer is on and logged-in

1 to 3 minutes

Confirm that your projector is turned on and is projecting properly

1 to 4 minutes

Confirm that all students’ computers are turned on and logged-in

3 to 5 minutes

Download and install Python 2.x (https://www.python.org/downloads) or navigate to Trinket (https://trinket.io/)

5 to 10 minutes

The Lesson

Warm-up Activity: Collecting data

10 minutes

Activity 1: Sorting data

10 minutes

Activity 2: Exploring algorithms

15 minutes

Wrap-up Activity: Understanding key concepts

5 minutes

Warm-up Activity: Collecting data (10 minutes)

Activity Overview: In this activity, students will gather meaningful and relevant content from each other using data collection. They will use the collected data in the following activities. 

Activity:

Collect class data, such as shoe size vs. height in inches, and enter it into a spreadsheet.

Sample Class Data:

Activity 1: Sorting data (10 minutes)

Activity Overview: In this activity, students will recognize patterns within a data set through the process of sorting the data based on various criteria (height and shoe size). They will use a spreadsheet or Python to organize a set of data. Students recognize that by sorting data, we can more easily generalize the pattern between height and shoe size.

Notes to the Teacher:

Instructions for how to organize a set of data using Google Sheets are:

Sort by shoe size:

  1. Highlight all of the data
  2. Click on the Data menu
  3. Click on Sort
  4. Sort by Shoe Size

Sort by height:

  1. Highlight all of the data
  2. Click on the Data menu
  3. Click on Sort
  4. Sort by Height

Sort by name:

  1. Click on the Name column
  2. Click Sort A-Z at the top of the column

Instructions for how to use Python to organize a set of data are:

  1. Open Python and type Ctrl-N (PC) or Command-N (Mac) to create a blank page.
  2. Save the data set above as StudentData.txt in the same directory as where the code below is saved. After entering the code, press F5 or from the menu Run → Run Module to run the code.

import re

#extracts the first column from the data

def GetName(list):

    return list[0]

#extracts the second column from the data

def GetHeight(list):

    return float(list[1])

#extracts the third column from the data

def GetShoe(list):

    return float(list[2])

#reads in the data from StudentData.txt

data = open('StudentData.txt','r')

data_search = re.findall('(\w+)\s(\d+)\s(\w+)', data.read())

#sorts the data by name

ordered_names = sorted(data_search, key=GetName)

#sorts the data by height

ordered_heights = sorted(data_search, key=GetHeight)

#sorts the data by shoe size

ordered_shoes = sorted(data_search, key=GetShoe)

#print out the results

print "Alphabetical Order\n"

for column in ordered_names:

    print column + "\n"

print "Sorted by height\n"

for column in ordered_heights:

    print column + "\n"

print "Sorted by shoe size\n"

for column in ordered_shoes:

    print column

Activity:

Have students sort the data by height and list the the five tallest students and the five shortest students. They can do this either by hand, electronic spreadsheet, or Python. Ask them the following questions:

Q1: Can you identify the tallest and shortest students faster before or after sorting the data? Repeat the process for the data on shoe size.

Q2: Name the people who are on both “top five” lists.  Name any people on the list of the five tallest, but not on the list of students with the largest shoe size.

Assessment:

A1: It is faster to identify the information that we wish to find after the data has been organized. By sorting data, we can more easily identify a pattern between height and shoe size. We can generalize the pattern if we notice that as height increases, shoe size also increases.

A2: Answers will vary. Students see how different patterns overlap, as well as the limits of some generalizations.

Activity 2: Exploring algorithms (15 minutes)

Activity Overview: In this activity, students design algorithms by decomposing a process into individual steps.

Activity:

Have students write an algorithm to calculate each of the following:

  1. the average height of your class
  2. the median height of your class
  3. the mode of the shoe sizes in your class
  4. the range of shoe sizes in your class  

Teaching Tips:

  • Divide students into small groups of 4-5 and have each group write a different algorithm. Have them test their algorithm on another group.

Assessment:

Writing an algorithm helps students to decompose a process into individual steps.

  1. average: add up the values and divide the sum by the number of values that you added.
  2. median: sort the data from least to greatest, fold your data set in half and circle the one or two pieces of data that lie on the fold.
  3. mode: tally up the number of times each data entry occurs to identify the piece of data that appears most frequently.
  4. range: sort the data from least to greatest and circle the two pieces of data at either end of the data set.

Activity:

Ask your students the following questions:

Q1: What step of the spreadsheet algorithm corresponds to the function “sorted( )” in Python?

Q2: In the line of code “orderednames = sorted(datasearch, key=GetName)“, which argument tells Python the name of the data that we want to analyze?

Q3: What part of the program instructs Python to sort by Height?

Q4: What part of the Python program most closely corresponds to hitting the [Return] or [Enter] key and seeing the data sorted in the spreadsheet?

Assessment:

A1: Clicking on Sort A-Z  from the data menu corresponds to the sorted function in Python. Students see that there is some overlap in the algorithm used to sort data in Excel and the built-in functions used in Python.

A2: The data from our spreadsheet is stored in a variable called datasearch, so when we type datasearch into the sorted function we are telling Python what data to sort. By focusing on a single line of code within a relatively lengthy program, students begin to understand the individual parts of a multi-step process (decomposition).

A3: The line orderedheights = sorted(datasearch, key=GetHeight)gets the data from datasearch, and runs it through the function GetHeight. Identifying the part of an algorithm that performs a specific function encourages students to focus on decomposing a multi-step process.

A4: The print statements instruct Python to print the answer to the screen similarly to how hitting [Return] or [Enter] instructs the spreadsheet to display the result. Students see how an algorithm in Python corresponds to an action in a spreadsheet.

Wrap-up Activity: Understanding key concepts (5 minutes)

Activity Highlights: In this activity, students will finish the lesson by demonstrating their understanding of the key concepts covered during this lesson.

Student Activity:

Journaling: Students respond to the following prompt in their journal or word processor:

In what other situations might you need to sort data and how would it help with data analysis? 

Learning Objectives and Standards

Learning Objectives

Standards

LO1: Students will be able to collect data from classmates and enter it into a spreadsheet.

Core Subject

CCSS MATH.CONTENT.HSS.ID: Interpreting Categorical and Quantitative Data.

Computer Science

CSTA L2.CT.14: Examine connections between elements of mathematics and computer science including binary numbers, logic, sets and functions.

LO2: Students will be able to sort data, using a spreadsheet or Python.

Computer Science

CSTA L3A.CT.4: Compare techniques for analyzing massive data collections.

UK 4.2: Develop and apply their analytic, problem-solving, design, and computational thinking skills.

LO3: Students will be able to recognize and generalize patterns in sorted data.

Computer Science

AUSTRALIA 10.4 (Collecting, managing and analyzing data): Analyse and visualise data to create information and address complex problems; and model processes, entities and their relationships using structured data.

CSTA L3B.CT.9: Analyze data and identify patterns through modeling and simulation.

LO4: Students will be able to write algorithms to calculate mean, median, mode, and range of a set of data

Computer Science

CSTA L2.CT.1: Use the basic steps in algorithmic problem-solving to design solutions (e.g., problem statement and exploration, examination of sample instances, design, implementing a solution, testing and evaluation).

UK 3.2: Understand several key algorithms that reflect computational thinking [for example, ones for sorting and searching]; use logical reasoning to compare the utility of alternative algorithms for the same problem.

Additional Information and Resources

Lesson Vocabulary

Term

Definition

For Additional Information

Algorithm

A series of instructions that can be repeated over and over with the same result for a given input (e.g. recipe, computer software, sheet of music notes).

http://en.wikipedia.org/wiki/Algorithm

Pattern

A discernible regularity in the world or in a man-made design (i.e. data)

http://en.wikipedia.org/wiki/Pattern 

Sorting

Any process of arranging items according to a certain sequence or in different sets, by either ordering or categorizing

https://en.wikipedia.org/wiki/Sorting 

Computational Thinking Concepts

Concept

Definition

Algorithm Design

Creating an ordered series of instructions for solving similar problems

Data Analysis

Making sense of data by finding patterns or developing insights

Data Collection

Gathering information

Decomposition

Breaking down data, processes or problems into smaller, manageable parts

Pattern Generalization

Creating models of observed patterns to test predicted outcomes

Pattern Recognition

Observing patterns and regularity in data

Extension Activities for Student Enrichment

Administrative Details

Contact info

For more info about Exploring Computational Thinking (ECT), visit the ECT website (g.co/exploringCT)

Credits

Developed by the Exploring Computational Thinking team at Google and reviewed by K-12 educators from around the world.

Last updated on

06/30/2015

Copyright info

Except as otherwise noted, the content of this document is licensed under the Creative Commons Attribution 4.0 International License, and code samples are licensed under the Apache 2.0 License.

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