The world is awash in data and there is a huge demand for people with the skills and knowledge to turn data into actionable insights. STAT6020 covers the basics of predictive data analytics, statistical computing and visualisation. Students develop an understanding of data science, from the basic skills of data processing and visualisation to building sophisticated descriptive and predictive models.
STAT6020 focuses on developing models for classification and prediction. The aim is to use a set of predictor variables to model the outcome of a target variable using techniques such as least squares and logistic regression, k-nearest neighbours, classification and regression trees (CART), and hierarchical classifiers. Shrinkage-based methods for model selection and cross-validation for model evaluation are also introduced. Students develop coding and reproducible reporting skills with open source software.
STAT6020 equips students with the data skills and acumen to excel in their chosen career through their ability to analyse a variety of data sources and make data driven decisions.
Availability2020 Course Timetables
- Semester 2 - 2020
On successful completion of the course students will be able to:
1. Apply classification and prediction modelling techniques to turn data into actionable insights
2. Perform model selection to identify the most important predictors out of a potentially very large set of predictor variables
3. Use cross-validation to assess the performance of selected models
4. Understand the importance of reproducible reporting and report on the results of a statistical analysis of a data set
5. Discuss the limitations of the statistical analyses considered
The course will include the following topics:
- Classification and prediction modelling techniques such as least squares and logistic regression, k-nearest neighbours, classification and regression trees (CART), and hierarchical classifiers
- Variable and feature selection techniques
- Cross-validation for model evaluation
- Statistical computing and reproducible reporting
Written Assignment: Written Assignment
Formal Examination: Formal Examination
Participation: Online discussion participation
Integrated Learning Session
Online 3 hour(s) per Week for Full Term starting in week 1