Master of Business Analytics (B6022) credits

Considering applying for credit for the Master of Business Analytics?

To support your application, review the topics listed against each unit below and provide a unit guide that shows you have covered the topics listed. The unit guide you provide must match the subject title (and the year it was studied) on your academic transcript.

Applications for credit are lodged online where you can upload the relevant unit guide and advise which unit(s) you are seeking credit for.

Undergraduate

If your undergraduate study is in a cognate area you may be eligible for exemption for the Advanced Preparatory units:

ETC5510 Introduction to data analysis

• Workflow and introduction to R
• Tidy data
• Wrangling verbs/operations
• Visualising data
• Missing value handling
• Working with temporal and spatial variables
• Data collection: scraping, JSON
• Basic statistical models: form and fitting and diagnosis
• Basic computational models: algorithm (regression & classification trees), stopping, diagnostics
• Text analysis: wrangling text into data, sentiment tagging
• Network modelling: Association matrices and simple network layout, with associated variables

ETC5242 Statistical thinking

• Intro to R/RStudio
• Intro to data, visualisation and wrangling
• Randomisation and simulation for testing proportions
• Resampling techniques for assessing variability in means
• Distributional models and maximum likelihood
• Updating discrete probabilities
• Bayesian inference for numerical data
• Decision rules
• Regression models
• Regression using randomisation or Bayesian methods
• Ensemble models

ETC5250 Introduction to machine learning

• Conceptual overview of ML, bias/variance, loss
• Resampling: LOOCV, cross-validation, training/test
• Dimension reduction: PCA, PP, MDS, regularisation
• High-d vis: tours, par coords, mosaic plots
• Regression: quantitative response
• Classification: categorical response, logistic, LDA, trees, SVM, NN, deep learning
• Ensembles and boosting: forest, xgboost
• Unsupervised classification: k-means, hierarchical clustering
• Model choice, diagnostics

ETC5550 Advanced forecasting

• Introduction to forecasting and R
• Time series graphics
• Time series decomposition
• The forecaster’s toolbox
• Exponential smoothing
• Forecasting with ARIMA models
• Multiple regression and forecasting
• Dynamic regression

Graduate

If you have completed study at postgraduate level study you may be eligible for credit.

If you have studied at postgraduate level but have not completed units to be considered for specified credit you may still be eligible for unspecified credit.

To be considered for specified credit your previous study should cover the topics listed under the relevant unit(s) below:

ETC5512 Wild-caught data

• Introduction to data collection methods
• Open data definitions, sources and examples
• Case study: macroeconomic data
• Data ethics
• Case study: monitoring the Great Barrier Reef
• Case study: Australian census and election data
• Case study: International student assessment data
• The language of designed experiments
• Case study: US air traffic (databases, maps)
• Case study: Tennis (web scraping)
• On the proper care and feeding of wild data

ETC5513 Collaborative and reproducible practices

• Reproducible reports: R markdown
• Introduction to version control systems: git and github
• Reproducible reporting using R markdown, git and github
• Collaborative practices: requests and issues
• Industry practices: guest lecture
• Scientific writing with latex using overleaf
• Git commits, stepping back, creating branches
• Automated bibliography, bibtex
• Dynamic documents: Rmarkdown, latex, pandoc how they coordinate
• Working with large files: git LFS
• Workflows and conflict resolution
• Communicating on projects: github issues
• Linking and referencing, for figures and tables
• Licensing and sharing
• Dependency management: packrat, renv, library paths
• Academic writing advice and principles

ETC5521 Exploratory data analysis

• Why this course? What is EDA?
• Learning from history, the original EDA with pencil and paper, early computational tools, from Tukey
• Initial data analysis: Model dependent exploration and how it differs from EDA
• Working with a single variable, making transformations, detecting outliers, using robust statistics
• Bivariate dependencies and relationships, transformations to linearise
• Going beyond two variables, exploring high dimensions
• Making comparisons between groups and strata
• Sculpting data using models, checking assumptions, co-dependency and performing diagnostics
• Exploring data having a space and time context
• Extending beyond the data, what can and cannot be inferred more generally, given the data collection
• Using computational tools to determine whether what is seen in the data can be assumed to apply more broadly

ETC5523 Communicating with data

• Advanced Reproducible Practices (rmarkdown, bookdown)
• Introduction to Web and Data Technologies (HTML/CSS/JS, htmlwidgets, crosstalk)
• Fashionable Model Output and Data Tables (broom, kableExtra, knitr, DT, formattable)
• Data Storytelling on the Web (blogdown, distill, git & github)
• Communicating Data with Interactive Web Apps (shiny, plotly)
• Effective Data Visualisation (ggplot, colorspace)
• Clearly Communicating with Code (devtools, usethis, testthat, roxygen2, pkgdown)
• Stylishly Communicating with Code
• Captivating Statistical Presentations (xaringan)
• Guest Lecture on Practice & Data Storyboard (posterdown)
• Data Dashboard (flexdashboard, shinydashboard)

ETF5550 High-dimensional data analysis

• Introduction
• Basic R
• Distance and Data (Euclidean Distance, Manhattan Distance, Jaccard Distance and some brief discussion - one slide - on collaborative filtering)
• Cluster Analysis (single linkage, complete linkage, average linkage, centroid method, Wards method, k-means)
• Multidimensional scaling (classical, Sammon mapping, Kruskal’s algorithm - a little bit on manifold learning)
• Principal Components Analysis (including PCA biplots)
• Factor models (what in social science is generally called “exploratory factor analysis” including orthogonal and oblique rotations)
• Matrix algebra: Eigen-decomposition, factor rotations
• Correspondence analysis

ETC5580 Advanced statistical modelling

• Graphics for linear models and interpreting interactions
• Leverage, influence, and residual analysis for linear models
• Regression with binary responses
• Regression with binomial and proportional responses
• Regression with count responses
• Generalized linear models
• Random effects
• Mixed-effects models
• Nonparametric regression
• Nonparametric inference
• Additive models