|
| 1.
| Apply mathematical statistics, including concepts of probability, random variables, standard distributions, transformations, sampling distributions, point and interval estimations, and hypothesis testing, to solve science, engineering or business related problems. |
| 2.
| Apply appropriate techniques of data visualization, data pre-processing, clustering, frequent pattern mining for small and large data sets to solve a range of scientific, engineering or business-related data analytic problems. |
| 3.
| Develop and critically evaluate empirical models from data using regression techniques. |
| 4.
| Apply classification algorithms to develop classification models for a given data set. |
| 5.
| Critically evaluate data ownership, privacy and ethical issues related to data analytics. |
| 6.
| Apply effective communication methods including assignment and practical reports, to convey ideas and principles. |
|
|
| Assessment Task | Value (of total mark) | Related Learning Outcome/s |
|---|
| 1. |
Data Analytics Project Report (15-20 pages) |
30% |
1,2,3,4 |
| 2. |
Online micro tests (2 x 30 minutes) |
30% |
1,2,3 |
| 3. |
Final test (2 hours) |
40% |
1,2,3,4,5,6 |
|
