ETF2121 · Data analysis in business

Overview

This unit provides an overview of fundamental tools of data and statistical analysis used in the Business and Economics disciplines. The methods covered are widely used in industry and academia, providing the necessary foundation for more advanced approaches as you advance your training and career. This unit delves deeper into basic statistical concepts, with a focus on their application in finance, accounting, and other sectors. It introduces cutting-edge software and programming languages such as R, Power-BI and SQL for robust data analysis and predictive modelling. You will engage with modern data sources and advanced sampling techniques, learn to perform hypothesis testing, and apply modelling techniques such as regression and time series analysis in business contexts. The unit places a strong emphasis on practical applications, aiming to equip you with the skills to interpret and leverage statistical data for solving business problems and making informed decisions in industry.

Offerings

Assessment

The Handbook lists an examination for this unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. apply industry-standard tools, including R, Power BI, and SQL, to query, process, analyse, visualise, and model data for business insights
2. implement statistical and machine learning techniques to identify patterns, generate predictions, and support data-driven decision-making
3. interpret and effectively communicate statistical findings to address complex business challenges and inform strategic decisions.

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.