Checkout a project for an overview or dig into a key challenge to find more detail on methods and outcomes

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    Statistics for Data Science

    University of Technology Sydney subject Statistics For Data Science took us through a challenging group project to collect, clean and engineer a dataset around a chosen topic. While encouraging us to evaluate how we can share this information with the DS community


    SEM and CFA Analysis of Implied Structure in COVID Project

    Assumptions made during previous work investigating COVID-19 data imply cause and effect relationships between variables. Using causal directed acyclic graphs and structural equation modelling we examine the structure and accuracy of the explicit and implicit assumptions.

    Animation More Better

    A short vignette on understanding advanced gganimate features and how they can improve your animated data visualisations

  2. laptop with a coffee next to it processing some information

    Data Algorithms and Meaning

    University of Technology Sydney subject Data, Algorithms and Meaning, focuses on developing and evaluating a variety of data science models in practical challenges and effectively communicating outcomes and value of these activities to the target audience


    Assignment 3 Text Analysis of an Unknown Corpus

    Communicating the outcomes of analysis of an unknown corpus of documents. Developing and comparing outcomes from ‘bag of words’, TFIDF, clustering, LSA, and LDA / topic models

    Assignment 2 Developing a Recommender for MovieLens100K

    Communicating the business value and potential utility of data insights and a recommender model. Comparing linear regression, tree, ensemble, clustering and SVD model outputs

    Assignment 1 Classification Modelling with Automotive Data

    Developing classification models to identify customers likely to repurchase from automotive data. Comparing GLMNET, random forest and xgboost predictions, selecting the best model and communicating outcomes to the audience

    Assignment 1 Linear Modelling with Sales Data

    Developing linear models with R to make sales predictions and creating a report that engaged the audience while communicating outcomes