Forecasting is a fundamental business skill. Forecasts of the future are used in all areas of business, from operations and finance to marketing and entrepreneurship. Predictive analytics is about using data to forecast uncertain quantities and events.
Data Analytics II introduces students to three key topics in predictive analytics: time series, regression, and ensembling, and develops students’ ability to think like a data scientist.
The module builds on ideas and tools introduced in MSIN102P Data Analytics I and MSIN204P Computational Thinking, including R and Tableau, statistical software used by the world’s leading data scientists.
During the module, students will work with example data sets to experience the stages of the data science process: they will visualise data, propose models that might fit the data, choose a best-fit model, use that model to make predictions, and test those predictions against new realisations.
Cases that illustrate the applications of data science to business problems will be used.
Upon successful completion of the module, students will be able to:
- Understand how organisations use predictive analytics to forecast uncertain quantities and events.
- Understand the stages of the data science process.
- Understand how to use time series, regression, and ensembling techniques to develop business forecasts.
- Characterise and critically assess the quality of data sets and their limitations in the context of data-driven decision-making.
- Understand how to source, cleanse and manage data sets.
- Use selected data analytics tools to manipulate and visualise data.
- Use selected data analytics tools to develop predictive analytics models.
- Predictive Analytics
- Time Series
- Machine Learning
- Data Analytics Thinking
- Data-Driven Decision-Making
- Data Science Process
- Data Visualisation
50% is awarded for Individual Coursework, 30% Group Coursework and 20% Group Coursework Scenario Weeks
Current students should refer to Moodle for specific details of the current year’s assessment.
An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. (Freely available online - details will be provided)
Practical Time Series Forecasting with R by Galit Shmueli and Casey Lichtendahl.
Forecasting: Principles and Practice by Rob Hyndman and George Athanasopoulos. (Freely available online - details will be provided)