Lesson Summary


Big Data has been defined in many different ways. Easy access to large sets of data and the ability to analyze large data sets changes how people make decisions. Students will explore how Big Data can be used to solve real-world problems in their community. After watching a video that explains how Big Data is different from how we have analyzed and used data in the past, students will explore Big Data techniques in online simulations. Students will identify appropriate data source(s) and formulate solvable questions.



  • Students will explain how analyzing Big Data is different from the way ordinary data is analyzed.
  • Students will describe how computers can make predictions and answer questions through the use of Big Data, storage of data, and processing data.
  • Students will synthesize the relationship(s) between causation and correlation.



Session 1- What is Big Data?


  • Getting Started (10 min) - Journal
  • Guided Activities (30 min) – Reading and Video
  • Wrap Up (10 min) – Group Review
  • Homework


Session 2 – Where can big data be used?


  • Getting Started (5 min) - Journal
  • Guided Activities (15 min) – Processing Big Data
  • Independent Activities (25 min) – Online Research
  • Wrap Up (5 min) – Exit Slip



Learning Objectives

CSP Objectives

Big Idea - Creativity
  • EU 1.2 - Computing enables people to use creative development processes to create computational artifacts for creative expression or to solve a problem.
    • LO 1.2.2 - Create a computational artifact using computing tools and techniques to solve a problem. [P2]
      • EK 1.2.2A - Computing tools and techniques can enhance the process of finding a solution to a problem.
    • LO 1.2.3 - Create a new computational artifact by combining or modifying existing artifacts. [P2]
      • EK 1.2.3A - Creating computational artifacts can be done by combining and modifying existing artifacts or by creating new artifacts.
      • EK 1.2.3B - Computation facilitates the creation and modification of computational artifacts with enhanced detail and precision.
    • LO 1.2.5 - Analyze the correctness, usability, functionality, and suitability of computational artifacts. [P4]
      • EK 1.2.5B - A computational artifact may have weaknesses, mistakes, or errors depending on the type of artifact.
      • EK 1.2.5C - The functionality of a computational artifact may be related to how it is used or perceived.
  • EU 1.3 - Computing can extend traditional forms of human expression and experience.
    • LO 1.3.1 - Use computing tools and techniques for creative expression. [P2]
      • EK 1.3.1A - Creating digital effects, images, audio, video, and animations has transformed industries.
Big Idea - Data
  • EU 3.1 - People use computer programs to process information to gain insight and knowledge.
    • LO 3.1.1 - Find patterns and test hypotheses about digitally processed information to gain insight and knowledge. [P4]
      • EK 3.1.1C - Combining data sources, clustering data, and data classification are part of the process of using computers to process information.
    • LO 3.1.3 - Explain the insight and knowledge gained from digitally processed data by using appropriate visualizations, notations, and precise language. [P5]
      • EK 3.1.3A - Visualization tools and software can communicate information about data.
      • EK 3.1.3B - Tables, diagrams, and textual displays can be used in communicating insight and knowledge gained from data.
  • EU 3.2 - Computing facilitates exploration and the discovery of connections in information.
    • LO 3.2.1 - Extract information from data to discover and explain connections or trends. [P1]
      • EK 3.2.1A - Large data sets provide opportunities and challenges for extracting information and knowledge.
      • EK 3.2.1B - Large data sets provide opportunities for identifying trends, making connections in data, and solving problems.
      • EK 3.2.1C - Computing tools facilitate the discovery of connections in information within large data sets.
      • EK 3.2.1D - Search tools are essential for efficiently finding information.
      • EK 3.2.1E - Information filtering systems are important tools for finding information and recognizing patterns in the information.
      • EK 3.2.1F - Software tools, including spreadsheets and databases, help to efficiently organize and find trends in information.
    • LO 3.2.2 - . Determine how large data sets impact the use of computational processes to discover information and knowledge. [P3]
      • EK 3.2.2B - The storing, processing, and curating of large data sets is challenging.
      • EK 3.2.2C - Structuring large data sets for analysis can be challenging.
      • EK 3.2.2D - Maintaining privacy of large data sets containing personal information can be challenging.
      • EK 3.2.2G - The effective use of large data sets requires computational solutions.
  • EU 3.3 - There are trade-offs when representing information as digital data.
    • LO 3.3.1 - Analyze how data representation, storage, security, and transmission of data involve computational manipulation of information. [P4]

Essential Questions

  • How can computing extend traditional forms of human expression and experience?
  • How are vastly different kinds of data, physical phenomena, and mathematical concepts represented on a computer?
  • How can computation be employed to help people process data and information to gain insight and knowledge?
  • How can computation be employed to facilitate exploration and discovery when working with data?
  • What opportunities do large data sets provide for solving problems and creating knowledge?

Teacher Resources

Student computer usage for this lesson is: required

Reading assignment and video clips for Session 1: 

Possibly useful resource(s) for data collection:

Big Data Concepts:

Sample data sets (both acquired from http://catalog.data.gov/dataset) :

  • FailedBanklist.csv
  • Consumer_Complaints.csv

Lesson Plan

Session 1 - What is Big Data?

Getting Started (10 min) - Journal

Journal: How can a computer gather data from people ? (Think-Pair-Share)

Remind students of the mind guessing game: http://en.akinator.com/ or 20 questions http://www.20q.net/ 

Discuss: How can the computer learn from people when playing one of these games? How many different answers do you think it could possibly know? 

Teacher note: students are not expected to actually play this game during class.

  • Students should document in their journal the answer to this question: How does this game store all of the possible answers?
  • Ask 3 students to share their answers. (Possible strategies to select a random student: random.com, or pick a random student name stick from a cup.)
  • This activity should lead into today’s lesson on how large amounts of data are stored and then accessed as needed in a system.

Guided Activities (30 min) - Reading & Video

Read The Rise of Big Data in chunks: An Introduction to “Big Data” (20 mins) Reading can be found at: http://www.foreignaffairs.com/articles/139104/kenneth-neil-cukier-and-viktor-mayer-schoenberger/the-rise-of-big-data

Break students into groups or pairs and jigsaw the seven units of the reading. Each group is to summarize their section in a tweet sized comment (not more than 140 characters).

Share tweets with the class.

Explain to students that big data is impacting every area of life. By using more data and processing power we can make better decisions. As an illustration, show a clip from the movie Moneyball: (3 mins) https://www.youtube.com/watch?v=rMObWsKaIls
After students watch, they create a journal entries explaining at least two ways data was used to better manage the baseball team. Partners discuss journal entries. Share at least one observation with table groups and then share at least one observation from each group with the class.

Show the first 3-5 minutes of this clip. (It becomes a bit dry, so just show the amount that is appropriate for your students to get the idea): https://www.youtube.com/watch?v=7D1CQ_LOizA


  • What are the 3 V's? List some details about each V.
  • (at 4:40) What is Hadoop and how is it used?
  • Identify appropriate data source and form questions
  • Extract data source into format supported by underlying tools
  • Normalize data (remove redundancies, irrelevant details)
  • Import data into tool
  • Perform analysis
  • Visualize results



Some other concepts to point out to students if there is time:

Some examples of how big data is used:


  • Netflix and Amazon use it to improve user recommendations
  • Dominos used it to determine that more people order pizza when it is raining so they now base some of their ad campaigns around weather patterns
  • Help police predict when and where crimes will appear


Some examples of how big data was inappropriately used:


  • In 2012 Target store's “outing” a teenager’s pregnancy
  • In 2012 Google spent 22.5 million on a settlement over allegations that they secretly tracked user’s web surfing
  • In 2012 Facebook spent 20 million to settle a lawsuit that alleged that they used user pictures without the user’s knowledge to endorse products that they “liked”
  • In 2013 the revelation of the NSA using big data for national security concerns




Wrap Up (10 min) – Group Review

  • Place students in groups of 4 where the first student is A, the next is B, etc. Each group creates a single sheet of paper with the letters across the bottom and the numbers 1 - 5 to demonstrate the level of understanding of each concept. Students should plot their understanding for each respective concept (see list below) on the graph. (The file "BigDataSampleDotGraph" in the lesson resources folder shows an example.)
  • Collect this graph as a level of student’s understanding of the concepts in the video.
  • Review each concept using the notes below.
    • Big data is kind of like drinking water from a fire hose. It's too much to process for a small pipeline…
    • A. The three “Vs”: Volume, Variety, and Velocity
    • B. Big Data processing steps:
    • C. Tools for processing big data:
      • Microsoft Excel (or some type of spreadsheet tool - i.e. Calc is another one)
      • Hadoop - a well known big data tool, provided by Apache, requires extensive programming knowledge to set up and use
      • SAS - provides a more intuitive interface and better graphical representations of data
      • Google Prediction API - takes advantage of machine learning to extract meaning from data
      • BitDeli - lightweight, easier to use version of Hadoop
    • D. Very few restrictions on use of big data
      • Companies collect large amounts of data on their customers
      • Can be sold to other companies
      • Can be sold to the government
      • Can be used to “de-anonymize” someone


Students are to pick three topics they want to research that use big data. It is preferred that these topics relate to something learned this year in the course (e.g., the need for IPv6). Tomorrow, as the students enter class, they will sign up on a list with their chosen topic. Since the students will have three options, it is likely they will get one of their selected topics to research.

Session 2 - Where can Big Data be used?

Getting Started (5 min) - Journal

Journal: Think about you daily and weekly activities. What types of data are being stored about you?

Remind students to think about what they do online, in stores, while in a car, etc.

Guided Activities (10 min) – Processing Big Data

Review the steps to processing Big Data:

  1. Identify appropriate data source and form questions
  2. Extract data source into format supported by underlying tools
  3. Normalize data (remove redundancies, irrelevant details)
  4. Import data into tool
  5. Perform analysis
  6. Visualize results

As a class, walk through these steps using the two files in the lesson resources folder (FailedBanklist.csv & Consumer_Complaints.csv)

Step 1.

Demonstrate how files such as these can be obtained at http://catalog.data.gov/dataset

Formulate questions such as:
Are there any banks that are on both the complaint list and the failed banklist?
Can we make some deductions about banks that may be on both lists? If so, what deductions can we make?

Step 2.

Extract data source into format supported by underlying tools

Open one of these files in Notepad (or some simple editing program such as Notepad++) and demonstrate how the actual data itself is separated by commas, thus the file name “csv” for comma separated value.

Open both files in Microsoft Excel. Complete a find for the bank name “Banco Popular de Puerto Rico” on both lists. You may want to first sort the data by bank name to find this bank or you can use CTRL + F to find the bank name (see screenshots below).

Step 3.

Normalize data (remove redundancies, irrelevant details)

In this step, there is technically no need to remove redundancies or irrelevant details but you can show the students how you could remove data or limit the data to a particular data set. For example, if were to want to look at only the banks from Maryland, you can use the filter tool to only view those banks from MD.

Step 4.

Import data into tool

Right now the file type is as a csv file. By resaving the file as a .xlsx file it becomes a true spreadsheet file. 

Step 5.

Perform analysis

We have determined that the bank “Banco Popular de Puerto Rico” is on both lists. Now ask the students “Why is this bank on both lists?” Note: On the Failed Bank list the Banco Popular de Puerto Rico is actually an acquiring institution. By looking more closely at the dates of the acquisition of the failed bank “Westernbank Puerto Rico” one can formulate some possible deductions that maybe the reason “Banco Popular de Puerto Rico” is on the complaint list is because they had recently taken over a failed bank. It could be possible that some of these complaints were related to this recent acquisition.

Step 6. 

Visualize Results

Explain to students that they will learn more about visualize their results in Unit 6. They can complete graph visualization in excel. Show them the website: http://www.gapminder.org/. Explain that even though a visualization in excel is not interactive like http://www.gapminder.org/, they can complete some form of visualizing their data by using a spreadsheet. Note: http://www.gapminder.org/ is VERY attention grabbing. Only briefly show the students what they can do with it (see how data changes over time, look at many different data sets, and download data in different forms - including csv and xlsx formats).

Independent Activity (30 min) – Online Research

Students should research their selected topics from homework. Some possible websites for finding data are listed above under “Possible good resource(s) for data collection.”

Students are to get your approval for a topic and then use the Big Data Sets Worksheet in the Lesson Resource Folder to find big data sets that are related to the approved topic. 

Wrap Up (5 min) – Exit Slip

Students are to review using http://www.gapminder.org/ looking specifically at life expectancy. Students will write one question after “playing” the timeline of life expectancy using gapminder on an exit slip before leaving class. For example, one may write “Why is the life expectancy of countries such as Denmark, Sweden, & Norway typically higher than other countries throughout most of the timeline?”

Evidence of Learning

Formative Assessment

Students are to submit a document stating their topic for research using Big Data. This document should answer the questions:

How is Big Data used to solve or remedy the topic?
Link(s) used to find Big Data? (i.e. data.gov, etc)

Summative Assessment

How has the transformation of data storage affected how data itself is used? 

Answer: Storage and processing of large digital data enables us to analyze large data sets quickly rather than small sampling sizes as used before. 

How can a computer use Big Data to make predictions? 

Answer: Computers can use smart algorithms, powerful processors, and clever software to make inferences and predictions for solvable questions.