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    Overview

    Application of advanced analytics in a business context using SAS. Topics include: data marts, data access and integration, predictive modelling, design of experiments, segmentation, forecasting.

    About this paper

    Paper title Advanced Business Analytics
    Subject Marketing
    EFTS 0.1667
    Points 20 points
    Teaching period(s) Semester 2 (Distance learning)
    Semester 2 (On campus)
    Domestic Tuition Fees ( NZD ) $1,196.41
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    Prerequisite
    BSNS 102 or BSNS 112 or STAT 110
    Eligibility
    Enrolments for this paper require departmental permission. View more information about departmental permission
    Contact
    damien.mather@otago.ac.nz
    Teaching staff

    Co-ordinator: Dr Damien Mather

    Paper Structure
    Topics include:
    • Basics of business analytics: thinking analytically and introduction to terminology
    • Classical statistics vs business analytics, data mining methodology
    • Predictive modelling
    • Introduction to design of experiments
    • Segmentation: case studies, cluster analysis, association analysis (market basket and sequence)
    • Forecasting concepts: case studies, time series models, marketing mix
    Teaching Arrangements
    Every week students must attend three 50-minute lectures and three 50-minute computer labs.
    Textbooks

    Required:
    Advanced Business Analytics Course Notes Volumes 1 and 2, The SAS Institute, 2012.

    Course outline
    View the course outline for MART 448
    Graduate Attributes Emphasised
    Critical thinking, Information literacy.
    View more information about Otago's graduate attributes.
    Learning Outcomes

    Students who successfully complete this paper will be able to:

    • Explain how modern data analytics are used to influence business decision making in a marketing context
    • Reliably select optimal methods and appropriately specify associated parameters of advanced analytical techniques comprising both supervised and unsupervised models, including clustering, regression trees and logit models using training, holdout and testing subsets
    • Apply those analytical tools and techniques and interpret the findings appropriately to address common business problems and needs comprising market insights, forecasts, segmentation, targeting and customer retention
    • Critically evaluate the quality of data preparation and the choice of an appropriate analytic technique from both theoretical and practical perspectives

    Timetable

    Semester 2

    Location
    Dunedin
    Teaching method
    This paper is taught through Distance Learning
    Learning management system
    Blackboard

    Computer Lab

    Stream Days Times Weeks
    Attend
    A1 Tuesday 11:00-11:50 29-35, 37-42
    Tuesday 15:00-15:50 29-35, 37-42
    Wednesday 10:00-10:50 29-35, 37-42

    Lecture

    Stream Days Times Weeks
    Attend
    A1 Monday 10:00-10:50 29-35, 37-42
    Tuesday 14:00-14:50 29-35, 37-42
    Wednesday 09:00-09:50 29-35, 37-42

    Semester 2

    Location
    Dunedin
    Teaching method
    This paper is taught On Campus
    Learning management system
    Blackboard

    Computer Lab

    Stream Days Times Weeks
    Attend
    A1 Tuesday 11:00-11:50 29-35, 37-42
    Tuesday 15:00-15:50 29-35, 37-42
    Wednesday 10:00-10:50 29-35, 37-42

    Lecture

    Stream Days Times Weeks
    Attend
    L1 Monday 10:00-10:50 29-35, 37-42
    Tuesday 14:00-14:50 29-35, 37-42
    Wednesday 09:00-09:50 29-35, 37-42
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