Marketing analytics involves the use of quantitative data to derive consumer insights, make decisions and evaluate the success of marketing strategies. Data science studies the computational principles, methods and processes for extracting information and knowledge from various types of data. It has been successfully used in many fields such as economics, finance, psychology, physics, and engineering, to provide insights into data as well as to support decision making. This course teaches students data analytical skills by introducing popular data science methods to support decision making and evaluation in marketing. It uses a combination of lectures and workshops. The course emphasises the practical applications and makes extensive use of R for marketing data visualization and analysis.

Teaching will be delivered through 6x3 hour sessions over a 3-week period in Semester 2. Each session is a combination of lecture and workshop practice.

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The assessment is an individually assessed assignment. Students will be given an assignment briefing document, datasets, and the assessment criteria grid based on the intended learning outcomes. Descriptions and grade category of each criterion will be given.  

This course aims to teach students the basic data science concepts, skills and tools to support decision making and evaluation in marketing analytics. It takes a very hands-on approach with real-world examples and equips students with skills and tools that can be used immediately for marketing analytics jobs.

By the end of this course students will be able to:

1. Clearly understand the types of analytics used in marketing and the impact of data-driven decision making

2. Effectively access and process various types of marketing data  

3. Appropriately summarise and visualise marketing data

4. Critically interpret and appraise the popular data science models for marketing analytics

5. Effectively use software tools to obtain and present analytical results from data science models

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.