1. Computing and Data Analysis
Unit 6

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Per 3: Mario G, Brian, Elena
Per 4: Daniel Dimitri Marcos

3. Lesson Topic
Students explore the pitfalls and challenges of putting together and managing large sets of data.
An overview of the final project is provided.

4. Today’s Lesson Objectives:
Explain the possible themes for the final project
Explain the differences between data used for making a case and data that informs discovery
Identify and discuss the considerations that must be made in order for a large data set to be useful
Consider how various types of data (numbers, text, dates, etc.) lend themselves to processing
Collaborate with others to create, manage, and maintain a large set of data.

5. Room Data Project See Piazza. Fill out the form for the next class.
So START TODAY, start with this class and the ones after today until we meet again.
Types of Data
Text
Spatial
Qualitative
Quantitative

6. Text Data Type
Made up of characters
Ansi Text

7. Spatial Data Type
Shapes
Longitude and latitude

8. Qualitative Categorical measurement Ex
Sports: Track, Swimming, Softball, Baseball, Badmiton
Rankings: good, bad

9. Quantatative Data Type
Numerical measurement
Weight: 100, 120, 150
Age: 13, 14,15,16,17,18,19

10. Room Data Proj. Cont.
Goal is for class to develop a single large set of data to use, formulate questions about the data, and answer them using an analysis tool.

11. Groups of four (4) See attached form on Piazza. Guide for Group
Merge personal data sets into one
Share their school/room data spreadsheets with each other and discuss the choices they made.
How was Happiness recorded?
How were dates/times written?
How were locations recorded?
Did everyone record the same number of entries for each day?
If the goal is to merge the group’s data into once consistent set, how might the number of entries per student be affected?

12. Groups of four(4)
It is likely that each person recorded their data differently.
Group’s task is to merge their four data sets into one consistent data set.
Group should agree how to denote days, times, locations and happiness.
It may be that some data needs to be compromised in order to conform to the group’s standard.
UNDER NO CIRCUMSTANCES should new data be “invented” after the fact to fit the mold.

13. Groups of four(4)
The group should also agree to how many records(rows) of data should be allocated for each person.
It need not be exactly the same number per person, but it should be representative of the group.
For example, if one student has a minute by minute account while another made entry for each class they were in, how can they make that consistent?

14. Group of four(4)
Once group agrees on standard, they must produce one file that represents everyone’s data from the group.
Students should use whatever collaboration technology is available to divide up the work of doing this--- you should not have one student re-typing all the data. Use Google docs so everyone can type on one single document.
Be done by end of class

15. Homework
Students should now collect data in the group’s agreed upon format.

16. Final Project Expectations
Distribute Data Analysis and Research Questions (Make copy and put it on Piazza)

17. Final Project Expectations
Class will select from one of the sample final project topics.
You could decide on each student group to choose a different topic, but this will mean that there is less student generated data for each.
You can choose a different topic from list.

18. Final Project Expectations
Each group will decide on the research questions to ask about the data and the story they hope to tell.
Design the survey to get at the information they need to tell the story
Gather data
Analyze their collected data and data from other sources.
Draw conclusions, augment, reinforce, tell a story, verify truths, call into question, or further discover and describe the topic.
Report their finds through a website or Scratch Project.

19. Final Project Expectations
Examine the available data set on each topic
Discuss possible research questions that might be answered or stories that might be told with additional information
(Note: give students Data Analysis and Research Questions online.)