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<CourseUnit xmlns="http://www.manchester.ac.uk/CUICourseUnitDetails" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.manchester.ac.uk/CUICourseUnitDetails.xsd">
  <UnitCode Applicant="Y" Label="Unit code" Student="Y">
    <Code>BMAN24621</Code>
  </UnitCode>
  <UnitTitle Applicant="Y" Label="Unit title" Student="Y">
    <Title>Business Data Analytics</Title>
  </UnitTitle>
  <MaxUnits Applicant="Y" Label="Credit rating" Student="Y">
    <Units>20</Units>
  </MaxUnits>
  <TeachingPeriods Applicant="Y" Label="Teaching period(s)" Student="Y">
    <Period>Semester 1</Period>
  </TeachingPeriods>
  <AcademicCareer Applicant="Y" Label="Academic career" Student="Y">
    <Value>Undergraduate</Value>
  </AcademicCareer>
  <UnitLevel Applicant="Y" Label="Unit level" Student="Y">
    <Level>Level 2</Level>
  </UnitLevel>
  <StaffList Applicant="Y" Label="Teaching staff" RoleLabel="Course Unit Role" Student="Y">
    <StaffMember>
      <Name>Yu-Wang Chen</Name>
      <Role>Unit coordinator</Role>
    </StaffMember>
  </StaffList>
  <OfferedBy Applicant="Y" Label="Offered by" Student="Y">
    <OrganisationList>
      <Organisation>
        <OrgName>Alliance Manchester Business School</OrgName>
      </Organisation>
    </OrganisationList>
    <GroupList>
      <Group>
        <GroupName></GroupName>
      </Group>
    </GroupList>
    <FheqLevels>
      <FheqLevel>
        <LevelNumber>1</LevelNumber>
        <LevelName>FHEQ level (Framework for Higher Education Qualifications) ' Middle part of Bachelors ' </LevelName>
      </FheqLevel>
    </FheqLevels>
    <Ects>
      <MaxUnits>European Credit Transfer &amp; Accumulation System Rating :   10.0</MaxUnits>
    </Ects>
  </OfferedBy>
  <MarketingOverview Applicant="Y" Label="Marketing Course unit overview" Student="">
    <Content>&lt;p&gt;The course covers a variety of data analytics techniques, including data management and preparation, data preliminary analysis and preprocessing, feature selection and engineering, predictive modelling, clustering, ensemble learning, association analysis, etc.&amp;nbsp;&lt;/p&gt;</Content>
  </MarketingOverview>
  <UnitOverview Applicant="" Label="Course unit overview" Student="Y">
    <Content>&lt;p&gt;The course covers a variety of data analytics techniques, including data management and preparation, data preliminary analysis and preprocessing, feature selection and engineering, predictive modelling, clustering, ensemble learning, association analysis, etc.&amp;nbsp;&lt;/p&gt;</Content>
  </UnitOverview>
  <Aims Applicant="Y" Label="Aims" Student="Y">
    <Content>&lt;p&gt;To provide students with an understanding&amp;nbsp; of data analytics for business&amp;nbsp; and management.&lt;/p&gt;&lt;p&gt;To help develop skills in the use of industry-leading software tools, mainly SAS packages.&lt;/p&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </Aims>
  <LearningOutcomes Applicant="Y" Label="Learning outcomes" Student="Y">
    <Content></Content>
  </LearningOutcomes>
  <Knowledge Applicant="Y" Label="Knowledge and understanding" Student="Y">
    <Content>&lt;p&gt;At the end of the course students should be able to:&lt;/p&gt;&lt;p&gt;•&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; Understand the fundamentals of data analytics and its applications to real life business problems,&lt;/p&gt;&lt;p&gt;•&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; Understand a variety of data analytics techniques, including data pre-processing, feature selection, predictive modelling, unsupervised learning, etc., and,&lt;/p&gt;&lt;p&gt;•&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; Demonstrate the ability to use specialised software tools to analyse large sets of data in different business contexts.&lt;/p&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </Knowledge>
  <IntellectualSkills Applicant="Y" Label="Intellectual skills" Student="Y">
    <Content></Content>
  </IntellectualSkills>
  <PracticalSkills Applicant="Y" Label="Practical skills" Student="Y">
    <Content></Content>
  </PracticalSkills>
  <TransferableSkills Applicant="Y" Label="Transferable skills and personal qualities" Student="Y">
    <Content></Content>
  </TransferableSkills>
  <EmployabilitySkillsList Applicant="Y" Label="Employability skills" Student="Y">
    <Skill>
      <SkillId></SkillId>
      <SkillDescription></SkillDescription>
    </Skill>
  </EmployabilitySkillsList>
  <Syllabus Applicant="Y" Label="Syllabus" Student="Y">
    <Content>&lt;p&gt;&amp;bull;&amp;nbsp;&amp;nbsp; &amp;nbsp;Introduction to business data analytics&lt;br /&gt;&amp;bull;&amp;nbsp;&amp;nbsp; &amp;nbsp;Data management and preparation,&lt;br /&gt;&amp;bull;&amp;nbsp;&amp;nbsp; &amp;nbsp;Data preliminary analysis,&lt;br /&gt;&amp;bull;&amp;nbsp;&amp;nbsp; &amp;nbsp;Data preprocessing,&lt;br /&gt;&amp;bull; &amp;nbsp;&amp;nbsp; &amp;nbsp;Predictive modelling&lt;br /&gt;&amp;bull;&amp;nbsp;&amp;nbsp; &amp;nbsp;Clustering analysis&lt;br /&gt;&amp;bull;&amp;nbsp;&amp;nbsp; &amp;nbsp;Ensemble learning&lt;br /&gt;&amp;bull;&amp;nbsp;&amp;nbsp; &amp;nbsp;Association analysis&lt;br /&gt;&amp;bull;&amp;nbsp;&amp;nbsp; &amp;nbsp;Text analytics&lt;br /&gt;&amp;bull;&amp;nbsp;&amp;nbsp; &amp;nbsp;Visual analytics and big data analytics&lt;/p&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </Syllabus>
  <TeachingMethods Applicant="Y" Label="Teaching and learning methods" Student="Y">
    <Content>&lt;p&gt;Two-hour lecture and two-hour lab per week (see detailed schedule below) for 11 weeks, directed reading and computer based support.&lt;/p&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </TeachingMethods>
  <AssessmentMethods Applicant="Y" Label="Assessment methods" Student="Y">
    <IntroText> </IntroText>
    <Method>
      <MethodId>0</MethodId>
      <MethodName>Other</MethodName>
      <MethodWeight>100%</MethodWeight>
    </Method>
    <OtherDescription>&lt;p&gt;Coursework project: group technical report (60%)&lt;/p&gt;&lt;p&gt;Individual executive summary (40%)&lt;/p&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;</OtherDescription>
  </AssessmentMethods>
  <FeedbackMethods Applicant="Y" Label="Feedback methods" Student="Y">
    <Content>&lt;p&gt;&amp;bull;&amp;nbsp;&amp;nbsp; &amp;nbsp;Informal advice and discussion during lectures or seminars.&lt;br /&gt;&amp;bull;&amp;nbsp;&amp;nbsp; &amp;nbsp;Responses to student emails and questions from a member of staff including feedback provided to a group via an online discussion forum.&lt;br /&gt;&amp;bull;&amp;nbsp;&amp;nbsp; &amp;nbsp;Written and/or verbal comments on assessed or non-assessed work.&lt;br /&gt;&amp;bull;&amp;nbsp;&amp;nbsp; &amp;nbsp;Generic feedback posted on Blackboard regarding overall examination performance.&lt;br /&gt;&lt;br /&gt;In addition to the central unit evaluation questionnaire, student are encouraged to give feedback through emails and conversations at anytime, and questionnaire near the end of the semester&lt;/p&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </FeedbackMethods>
  <RequirementsList Applicant="Y" Label="Pre/co-requisites" Student="Y">
    <Requirement>
      <UnitCode></UnitCode>
      <UnitTitle></UnitTitle>
      <RequirementType></RequirementType>
      <Description></Description>
    </Requirement>
    <Requirement>
      <UnitCode>BMAN10960</UnitCode>
      <UnitTitle>Quantitative Methods for Business and Management</UnitTitle>
      <RequirementType>Pre-Requisite</RequirementType>
      <Description>Compulsory</Description>
    </Requirement>
    <Requirement>
      <UnitCode>BMAN11060</UnitCode>
      <UnitTitle>Fundamentals of Data Analytics</UnitTitle>
      <RequirementType>Pre-Requisite</RequirementType>
      <Description>Compulsory</Description>
    </Requirement>
    <AdditionalRequirement>BMAN24621 has pre-requisites of: BMAN10960 or BMAN11060. Only available to students on: Mgt/Mgt Specialism; IMABS and IM. Core for BSc ITMB.&lt;p&gt;This course requires analytical thinking, the use and interpretation of mathematical &amp;amp; statistical concepts, as well as rapid familiarization with a range of specialist software tools. As such, students are expected to bring basic competency and confidence in all the above three areas, including a willingness for extensive independent study in line with the requirements for a 20-credit course unit.&amp;nbsp;&lt;br /&gt;For students progressing from BMAN10960 Quantitative Methods for Business &amp;amp; Management, it is strongly suggested that a mark of 60% or more should have been achieved.&lt;/p&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;</AdditionalRequirement>
  </RequirementsList>
  <AcademicPrograms Applicant="Y" Label="Academic programmes" Student="Y">
    <AcademicProgram>
      <Program></Program>
      <Plan></Plan>
      <Level></Level>
      <Requirement></Requirement>
    </AcademicProgram>
  </AcademicPrograms>
  <FreeChoice Applicant="Y" Label="Available as a free choice unit?" Student="Y">
    <Content>N</Content>
  </FreeChoice>
  <Accreditation Applicant="Y" Label="Accreditation" Student="Y">
    <Content></Content>
  </Accreditation>
  <RecommendedReading Applicant="Y" Label="Recommended reading" Student="Y">
    <Content>&lt;p&gt;Galit Shmueli,&amp;nbsp;et al.; Data Mining for Business Analytics: Concepts, Techniques, and Applications - in R (e-book available from the university library) or in Python, John Wiley &amp;amp; Sons, 2018.&lt;/p&gt;&lt;p&gt;Max Bramer, Principles of Data Mining, Springer, 2013.&lt;/p&gt;&lt;p&gt;Other reading materials will be shared via Blackboard.&lt;/p&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </RecommendedReading>
  <StudyHours Applicant="Y" Label="Study hours" Student="Y">
    <IntroText> </IntroText>
    <ScheduledHours Applicant="Y" Label="Scheduled activity hours" Student="Y">
      <ActivityHours>
        <ActivityType>Lectures</ActivityType>
        <Hours>22</Hours>
      </ActivityHours>
      <ActivityHours>
        <ActivityType>Practical classes &amp; workshops</ActivityType>
        <Hours>22</Hours>
      </ActivityHours>
    </ScheduledHours>
    <PlacementHours Applicant="Y" Label="Placement hours" Student="Y">
      <ActivityHours>
        <ActivityType></ActivityType>
        <Hours>0</Hours>
      </ActivityHours>
    </PlacementHours>
    <TotalHours Applicant="Y" Label="Independent study hours" Student="Y">
      <Hours>156</Hours>
    </TotalHours>
  </StudyHours>
  <Notes Applicant="Y" Label="Additional notes" Student="Y">
    <Content>&lt;p&gt;Pre-requisites: BMAN11060 Fundamentals of Data Analytics for BSc ITMB, and BMAN10960 Quants for Business and Management (except BSc Mathematics and Management &amp;amp; Maths Stats &amp;amp; OR.) or equivalent for other BSc programmes&lt;br&gt;Co-requisites: None&lt;br&gt;Dependent courses: None&lt;br&gt;Programme Restrictions:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;BSc Information Technology Management for Business&lt;/li&gt;&lt;li&gt;BSc Management and Management (Specialisms),&lt;/li&gt;&lt;li&gt;BSc International Management with American Business Studies,&lt;/li&gt;&lt;li&gt;BSc International Management,&lt;/li&gt;&lt;li&gt;BSc Mathematics and Management, Maths, Stats &amp;amp; OR&lt;/li&gt;&lt;/ul&gt;&lt;p style="color:rgb(60, 60, 60);font-family:Arial, sans-serif;"&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </Notes>
</CourseUnit>
