Group Presentation 2015 / Group Communications The Jotun Group Group Presentation 2015.
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Transcript of COMP1900 group presentation
![Page 1: COMP1900 group presentation](https://reader035.fdocuments.in/reader035/viewer/2022081603/558ff5591a28abe6798b45c0/html5/thumbnails/1.jpg)
TopicsTopicsData – SpreadsheetManipulating data
- Pivot tables - Visualisation(static and dynamic)
Comparing spreadsheet & databaseSupporting a hypothesis using dataSpreadsheet & database – which is
more appropriate for supporting hypothesis?
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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
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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
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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%
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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
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Static representationStatic representationWeekly Category total per person
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Static continued…Static continued…
But what if too many graphs are needed?
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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
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Dynamic Continued…Dynamic Continued…Hence we resort to dynamic
representationHere is one about Daily Total
Category per Person produced using Google docs
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StructureStructure
SpreadsheetsTable made up of individual cells
DatabasesCollection of tables storing related dataEach table contains columns/fieldsAlso Queries, reports, forms
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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
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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
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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
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Data ManipulationData ManipulationSpreadsheetsStatic and dynamic visualizationsPivot TablesExtensive mathematical calculations
DatabasesFew graphical visualizationsQueries, reportsLimited calculation functions in reports
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Reports exampleReports example
This report based the this query
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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
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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
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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.
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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
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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
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Working a lot over 10 hr correlated with low GPA
However, non-working person achieved high results
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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
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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
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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?
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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)
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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)
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Working vs Non-working studentsHypothesis unsupported by our
data
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Working vs Non-working students
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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
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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
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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