| 1 | <p>Describe the 'tidy data' abstraction and its importance to data management and analysis</p> | <p>MDataSci - Master of Data Science - Graduate Profile <p><strong style="color: rgb(73, 80, 87);">Knowledge and Practice</strong></p><p><strong style="color: rgb(73, 80, 87);">Critical Thinking</strong></p><p><strong style="color: rgb(73, 80, 87);">Solution-Seeking</strong></p> </p> |
| 2 | <p>Explain over-fitting and cross-validation and why they are important in flexible predictive modelling</p> | <p>MDataSci - Master of Data Science - Graduate Profile <p><strong style="color: rgb(73, 80, 87);">Knowledge and Practice</strong></p><p><strong style="color: rgb(73, 80, 87);">Critical Thinking</strong></p><p><strong style="color: rgb(73, 80, 87);">Solution-Seeking</strong></p><p><strong style="color: rgb(73, 80, 87);">Communication</strong></p> </p> |
| 3 | <p>Fit predictive linear regression models to real data sets and evaluate their accuracy</p> | <p>MDataSci - Master of Data Science - Graduate Profile <p><strong style="color: rgb(73, 80, 87);">Knowledge and Practice</strong></p><p><strong style="color: rgb(73, 80, 87);">Critical Thinking</strong></p><p><strong style="color: rgb(73, 80, 87);">Solution-Seeking</strong></p> </p> |
| 4 | <p>Explain the concepts of ensembles and regularisation and their important in predictive modelling</p> | <p>MDataSci - Master of Data Science - Graduate Profile <p><strong style="color: rgb(73, 80, 87);">Knowledge and Practice</strong></p><p><strong style="color: rgb(73, 80, 87);">Critical Thinking</strong></p><p><strong style="color: rgb(73, 80, 87);">Solution-Seeking</strong></p><p><strong style="color: rgb(73, 80, 87);">Communication</strong></p> </p> |
| 5 | <p>Fit tree-based models to real data sets and evaluate their accuracy</p> | <p>MDataSci - Master of Data Science - Graduate Profile <p><strong style="color: rgb(73, 80, 87);">Knowledge and Practice</strong></p><p><strong style="color: rgb(73, 80, 87);">Critical Thinking</strong></p><p><strong style="color: rgb(73, 80, 87);">Solution-Seeking</strong></p> </p> |
| 6 | <p>Discuss the individual and social impacts of widespread use of accurate and inaccurate predictive models, and the ethical implications for data scientists</p> | <p>MDataSci - Master of Data Science - Graduate Profile <p><strong style="color: rgb(73, 80, 87);">Knowledge and Practice</strong></p><p><strong style="color: rgb(73, 80, 87);">Critical Thinking</strong></p><p><strong style="color: rgb(73, 80, 87);">Solution-Seeking</strong></p><p><strong style="color: rgb(73, 80, 87);">Communication</strong></p><p><strong style="color: rgb(73, 80, 87);">Ethics and Professionalism</strong></p> </p> |
| 7 | <p>Fit neural network models to real data sets and evaluate their accuracy</p> | <p>MDataSci - Master of Data Science - Graduate Profile <p><strong style="color: rgb(73, 80, 87);">Knowledge and Practice</strong></p><p><strong style="color: rgb(73, 80, 87);">Critical Thinking</strong></p><p><strong style="color: rgb(73, 80, 87);">Solution-Seeking</strong></p> </p> |
| 8 | <p>Prepare data in the form needed for modelling when given a data set and relevant domain information</p> | <p>MDataSci - Master of Data Science - Graduate Profile <p><strong style="color: rgb(73, 80, 87);">Knowledge and Practice</strong></p><p><strong style="color: rgb(73, 80, 87);">Critical Thinking</strong></p><p><strong style="color: rgb(73, 80, 87);">Solution-Seeking</strong></p> </p> |
| 9 | <p>Choose an appropriate modelling technique and feature set and explain the choice when given a data set and relevant domain information</p> | <p>MDataSci - Master of Data Science - Graduate Profile <p><strong style="color: rgb(73, 80, 87);">Knowledge and Practice</strong></p><p><strong style="color: rgb(73, 80, 87);">Critical Thinking</strong></p><p><strong style="color: rgb(73, 80, 87);">Solution-Seeking</strong></p> </p> |