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RIGHT.This one is the one for the assignment.Don't comment on it I really do not care about anything you have to say.Of course i doubt anyone would even notice this or care about it so a resounding 'MEH' all round.TRANSCRIPT
TopicsTopicsData – SpreadsheetManipulating data
- Pivot tables - Visualisation(static and dynamic)
Comparing spreadsheet & databaseSupporting a hypothesis using dataSpreadsheet & database – which is
more appropriate for supporting hypothesis?
Part of Excel SpreadsheetPart of Excel SpreadsheetData regarding 2 weeks of group
members activities
Week DateUQ student
No. First Name Last Name Category Activity Time Duration
1 14/09/2008 41201396 Andrew McMillen Recreation Drinking 12:00:00 AM 6
1 14/09/2008 41201396 Andrew McMillen Recreation Drinking 3:00:00 PM 3
1 14/09/2008 41201396 Andrew McMillen Socialising Video Games 7:00:00 PM 2
1 14/09/2008 41201396 Andrew McMillen Education Internet 10:00:00 AM 4
1 14/09/2008 41201396 Andrew McMillen Travel Train 2:00:00 PM 1
1 14/09/2008 41201396 Andrew McMillen Rest Sleeping 11:00:00 PM 1
1 14/09/2008 41613298 Harry Kim Rest Sleeping 12:00:00 AM 91 14/09/2008 41613298 Harry Kim Religion Church 10:30:00 AM 2
1 14/09/2008 41613298 Harry Kim Recreation Videos 4:00:00 PM 3
1 14/09/2008 41613298 Harry Kim Education Study 8:00:00 PM 3
1 14/09/2008 41613298 Harry Kim Recreation Reading 12:00:00 AM 0.5
1 14/09/2008 41788468 Samuel Ninness Rest Sleeping 12:00:00 AM 9
1 14/09/2008 41788468 Samuel Ninness Recreation Videos 10:00:00 AM 8
1 14/09/2008 41788468 Samuel Ninness Recreation Videos 8:00:00 PM 2
1 14/09/2008 41788468 Samuel Ninness Rest Sleeping 10:00:00 PM 2
Pivot TablesPivot Tables
Weekly Duration
Week 1 Week 2
Andrew Harry Samuel Andrew Harry Samuel
Education 18 48 24.5 11.5 41 28.5
Exercise 2 1 1 3.5
Housework 3 2.5 2 2.5
Recreation 21 14 28.5 16 12.5 21
Religion 2 2
Rest 54 55.5 70 52.2 57.5 69
Socialising 6 10.5 4.5 16 9.65 4.5
Travel 6 10 8.3 5 9 8
Work 36 8.5 38 16
Manipulating data to for specific goalsE.g. Comparing Weekly Totals of
Category per personData much more useful
In PercentageIn Percentage
Week 1 Week 2
Weekly Duration Weekly Duration
Andrew Harry Samuel Andrew Harry Samuel
Education 20% 53% 27% Education 14% 51% 35%
Exercise 67% 33% 0% Exercise 22% 78% 0%
Housework 55% 0% 45% Housework 44% 0% 56%
Recreation 33% 22% 45% Recreation 32% 25% 42%
Religion 0% 100% 0% Religion 0% 100% 0%
Rest 30% 31% 39% Rest 29% 32% 39%
Socialising 29% 50% 21% Socialising 53% 32% 15%
Travel 25% 41% 34% Travel 23% 41% 36%
Work 81% 0% 19% Work 70% 0% 30%
Visualization Visualization Another way of manipulating dataLike pivot tables, allows data to be
represented in a useful wayDisplays data graphically e.g. Graphs2 types: Static and Dynamic
visualization
Static representationStatic representationWeekly Category total per person
Static continued…Static continued…
But what if too many graphs are needed?
Dynamic RepresentationDynamic Representation“Dynamic” – non-static
visualisationE.g. Daily Total Category per
person - Over 2 weeks, 14 graphs are
needed!So static visualisation is
inappropriate in certain cases
Dynamic Continued…Dynamic Continued…Hence we resort to dynamic
representationHere is one about Daily Total
Category per Person produced using Google docs
StructureStructure
SpreadsheetsTable made up of individual cells
DatabasesCollection of tables storing related dataEach table contains columns/fieldsAlso Queries, reports, forms
Database Structure Database Structure ExampleExample
Activity Log
Student Info
UQ student
No.First
NameLast
Name41201396 Andrew McMillen41613298 Harry Kim41788468 Samuel Ninness
Date UQ student
No. Activity Time Duratio
n 14/09/2008 41201396 Train 2:00:00 PM 1
14/09/2008 41201396 Sleeping11:00:00
PM 114/09/200
8 41613298 Sleeping12:00:00
AM 9
14/09/2008 41613298 Church10:30:00
AM 2
Category Activity Travel TrainRest Sleeping
Religion ChurchRecreation Videos
Activity Types
Relationships between similar data in tables
Field
Additional ConstraintsAdditional ConstraintsSpreadsheetsEnforces data format constraintsNumerical, currency, date/time, text formats
DatabasesSame formats as spreadsheetsAlso minimum and maximum field size,
required field, default values assigned and validation rules
Spreadsheet constraints Spreadsheet constraints exampleexample
Numbers Text Date Currency
1.00 Where 19/09/2009 $13.00
5423.00 Is 19/09/2009 $56.00
234.00 My 19/09/2009 $47.00
52.00 Cow 19/09/2009 $85.00
76.00 ? 19/09/2009 $99.00
Each Column is formatted to display
the specified information only
Data ManipulationData ManipulationSpreadsheetsStatic and dynamic visualizationsPivot TablesExtensive mathematical calculations
DatabasesFew graphical visualizationsQueries, reportsLimited calculation functions in reports
Reports exampleReports example
This report based the this query
Calculations ExampleCalculations Example
Num A Num B Total
20 2 22
10 6 16
5 5 10
7 3 10
9 8 17
Num A + Num B = Total
LimitationsLimitationsSpreadsheetsData in large spreadsheet systems
redundant and unreliableMultiple copy complicationsOne user at a time on centrally stored
spreadsheets
DatabasesEliminates spreadsheet problemsChanging user requirements necessitates a
new database
DevelopmentDevelopmentSpreadsheetsSimple to create.Requires considerable user maintenanceMultiple spreadsheets -> inconsistencies
occur
Databasesconsiderable time and energy to create. Little maintenance neededneed to be replaced when they become
outdated.
Supporting Hypothesis using Supporting Hypothesis using datadataHypothesis: It is argued that
students who have no more than 10 hours of paid work a week are more effective than students who do not work or work longer hours
Using our group dataUsing our group data
Excessive work correlates with lower time into education
However, one non-working person put a large amount of time into education
Working a lot over 10 hr correlated with low GPA
However, non-working person achieved high results
SummarySummaryBoth cases show mixed resultsHence data does not (fully)
support the hypothesis Non-working person had higher
Education hours and grade then someone close to 10 hr of working
Working vs Non-working students continued (data from another research)Dr Kerri-Lee Krause, Sept 200521st century undergraduate
student engaged, inert, or otherwise occupied?
Engagement = time, energy and resources devoted to uni activities
Working vs Non-working studentsHypothesis:No more than 10 hours of paid work a week = more effective than students who do not work, or work longer hours?
Working vs Non-working studentsKrause et al 2004: The First Year
Experience in Australian Universities: Findings from a decade of national studies
‘Effective’ student = more ‘engaged’ student
This = more time, energy and resources devoted to uni activities. (in theory)
Working vs Non-working studentsPaid students study less (10.5 hours)
than non-employed (11.8 hours per week)
Average uni contact hours per week for full-time first year students has declined to 16 per week in 2004 - was 17.6 in 1995
Paid part-time workers = fewer weekly contact hours (15.5) compared to their non-employed peers (16.8 hours per week)
Working vs Non-working studentsHypothesis unsupported by our
data
Working vs Non-working students
Spreadsheet vs DatabaseSpreadsheet good for the
purposes of this small-scale project
Easily create visualisations using graphs and pivot tables
If project was larger, recommend DBMS for stability, versatility and relational capabilities
Project LimitationsProject LimitationsCategorisation of activities sometimes
confusingUni grades vs work experience?Vague task descriptionsSmall sample size – not indicative of
habits across entire semesterHence unrepresentative of the whole
population of first year studentsOur group was unable to find statistics
regarding work and education
In Conclusion:In Conclusion:Comparison between parts of the
assessment were enjoyableOnline collaboration is highly
recommendedThis opens the door to further
research – are you interested?Cheers