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.
| Paper title | Advanced Business Analytics |
|---|---|
| Paper code | MART448 |
| Subject | Marketing |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period(s) | Semester 2
(Distance learning)
Semester 2 (On campus) |
| Domestic Tuition Fees (NZD) | $1,163.90 |
| 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
- More information link
- View more information about 400-level Marketing papers
- 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
