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BCO5010: Business Intelligence Technologies (Assignment 1)CARDIFF SCHOOL OF MANAGEMENT: ASSIGNMENT FEEDBACK PROFORMASTUDENT NAME:PROGRAMME: BSc Business Information Systems/BSc Software Development/ BSc ComputingSTUDENT NUMBER:YEAR: 1GROUP:Module Number: BCO5010Term: 1Module Title: Business Intelligence TechnologiesTutor Responsible For Marking This Assignment: Imtiaz KhanModule Leader: Imtiaz KhanAssignment Due Date: 18 Dec 2016 (via Moodle)Hand In Date: ASSIGNMENT TITLE: Analysis of locational social media data (Assignment 1) 42.5% of total marksSECTION A: SELF ASSESSMENT (TO BE COMPLETED BY THE STUDENT)In relation to each of the set assessment criteria, please identify the areas in which you feel youhave strengths and those in which you need to improve. Provide evidence to support your self-assessment with reference to the content of your assignment.STRENGTHSAREAS FOR IMPROVEMENTI certify that this assignment is a result of my own work and that all sources have beenacknowledged:Signed:______________________________________________ Date___________________________SECTION B: TUTOR FEEDBACK(based on assignment criteria, key skills and where appropriate, reference to professionalstandards)STRENGTHSAREAS FOR IMPROVEMENT AND TARGETS FOR FUTURE ASSIGNMENTSMARK/GRADE AWARDEDDATE:SIGNEDASSIGNMENT MODERATED BY:DATE
BCO5010: Business Intelligence Technologies (Assignment 1)MODERATOR’S COMMENTS:1. Learning outcomesBy completing this assignment the student will learn aboutSocial media data analysis with Excel and creating dashboard for reporting.Apply OLAP approach in conjunction with database for reporting.Design, program and link social media data with geospatial data.Critical analysis and evaluation of results.2. Assignment outline and guidance notesAnalysis of locational social media dataCase study: Monmouthshire County Council Outline:Monmouthshire County Council (MCC) would like to increase their organisational understanding andpotential impact of existing and potential online networks. They are pioneering a more relaxed and informal approach to talking with residents and developing relationships with other stakeholders. Aspart of its programme of culture change (outlined here: http://www.yc-yw.co.uk/) the council is empowering staff and residents toinnovate and find new ways of co-creating a better place to live.MCC have now conducted a pilot study to find out the opinions of council residents and have gathered a large dataset of social media data. They asked residents to describe how they thought MCC could best improve their services. MCC were not only interested in the topics broached, but also the style of the respondents’ narrative. They developed some calculated fields, and now they have asked you to finish off the analysis of their data and report on your analysis.A detailed summary of the data can be found in Appendix A, however in summary it contains the following:
BCO5010: Business Intelligence Technologies (Assignment 1)FieldsExplanationidUnique ID for residentformality, flesch, fog, kincaid, percentComplexWords, syllablesPerWords, wordsPerSentence, wordcountA series of fields all related to the structure and style of the residents responsesexSex of the residentextraversion, emotional stability, agreeableness, conscientiousness, openness to experienceA series of fields corresponding to the “Big 5” personality traitslongitude/latitudeGeographical location of residenteducation, jobs and employment, recycling and waste, buses and public transport, planning and housing, care and support, activities and leisureThe topics the resident mentioned.You have been provided an Excel spreadsheet with several thousand rows of data (each row is a unique resident), in the above format. Additionally you have access to KML files for Gwent boundary and neighbourhoods.2.1 Assignment tasks:Statistical analysis [20%]Using the Excel spreadsheet:Create a dashboard to view a selection of the data in a more synthetic and operationally useful way.A table showing the mean and standard deviation for each featureAny appropriate functions in Excel (e.g. frequency distributions and polygons, overlaying ‘curves of best fit’ etc) to explore:oM1 versus literacy fieldsoM1 versus M2 and/or M3oAny significant relationships between M4, M5, M6 and M7OLAP [20%]Import the data from the spreadsheet into an Access database.Create two appropriate queries and supporting reports.Using PowerPivot1 (or any other tool suitable for the version of Microsoft Office) create any appropriate OLAP cubes2 in Excel using the Access database as the data source1 http://www.powerpivotpro.com/2 http://www.powerpivotpro.com/2010/06/using-excel-cube-functions-with-powerpivot/
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