ETB2111 · Business data analytics

Overview

Data is collected with an intended purpose for analysis and can provide the “why” behind patterns identified through data analytics. This unit further develops statistical concepts covered in ETB1100 Business Statistics and centres on the analysis of data that is readily accessible in businesses across all sectors. You will learn tools relevant across the whole process of data analysis from appropriate sample size calculations and collection, to mining data for high level business insights, through to deep dive analytics where inferences are drawn and tested for significance, and relevant predictive models are identified, applied and validated. Specifically, this unit covers data visualization for numeric and categoric data; sampling theory and design; statistical inference as a means to identifying significant findings around means and proportions; using multiple regression to analyses relationships amongst variables; using classification and regression trees to make predictions. Emphasis throughout is on translating results into readily digestible, actionable insights in context of the business needs at hand. Widely available software such as Excel will be used, with an introduction to R and RStudio which focuses on application rather than coding.

Offerings

Assessment

The Handbook lists an examination for this unit.

Requisites

Learning outcomes

1. develop effective visualisations to uncover and understand relationships within data sets
2. learn how to identify and collect a statistically valid, representative sample of data that satisfies its intended purpose
3. demonstrate an understanding of the importance of statistical inference in business, specifically, be able to determine and interpret significant differences required for decision making
4. demonstrate the ability to conduct and understand regression analyses and classification and regression trees
5. effectively interpret and communicate the results of your investigations to the appropriate stakeholders in order to support data driven decision making in business.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

Student experience, not official rules. Nothing here changes what the Handbook says.