Course Description

In today's world, good decision-making relies on data and data analysis. This course helps students develop the understanding that they will need to make informed decisions using data and to communicate the results effectively. The course introduces students in business, economics, and related disciplines to the essential concepts, tools, and methods of statistics. The focus of the course is on concepts, reasoning, interpretation and thinking rather than computation, formulae and theory. The course covers two main branches of statistics: descriptive statistics and inferential statistics. At the end of the course, students will know how to use statistics, the limitations of statistical inference and the ethics of data analysis and statistics.

Learning Outcomes

• Apply correctly a variety of statistical techniques, both descriptive and inferential.
• Interpret, in plain language, the application and outcomes of statistical techniques.
• Interpret computer output and use it to solve problems.
• Recognise inappropriate use or interpretation of statistics in other courses, in the media and in life in general and comment critically on the appropriateness of this use of statistics.

Learning Experience

Data analytics i is an introductory statistics subject designed to help students make informed decisions using data and communicate results clearly. It covers both descriptive and inferential statistics, progressing from data collection and visualisation through probability and hypothesis testing, and into simple modelling concepts such as correlation and linear regression. A central design intent was to prioritise concepts, reasoning, and interpretation over manual calculation, while still guiding students through necessary computation and terminology to build confidence and a shared language.

The learning experience was structured across modules with an intentional shift in learner focus over time. Early modules emphasised “feeling how the data behave” through visual and descriptive techniques. Mid-course content introduced more formal statistical language and inference concepts, and later modules moved into modelling and interpretation. This progressive structure supported students from varied backgrounds (business, economics, health, and beyond), many of whom enter the course with low confidence in maths and statistics.

A key feature of the design was the use of multimodal explanations to make abstract ideas tangible. Complex processes such as sampling plans (simple random, stratified, and cluster sampling) were clarified through short animations that reduced reliance on dense text. These animations used simple geometric representations and narrated explanation to show how sampling choices influence bias and error, supporting conceptual accuracy without overwhelming learners with notation. H5P presentations were also used strategically to chunk challenging topics into smaller, checkable steps.

Interactivity was used to strengthen intuition and support transfer to assessment. A simulation-based tool for coin toss sampling distributions enabled students to generate data, observe sample means stabilising with larger samples, and then build a sampling distribution from repeated simulations. This created a concrete bridge between probability, sampling, and inference, helping students understand what a sampling distribution represents rather than memorising a definition.

Formative practice was also supported through an embedded “streak test” activity, adapted from earlier University of Adelaide MOOC development work and integrated into Canvas. Rather than a single, static quiz attempt, the streak test used randomised, resettable question sets that students could repeat until they achieved a correct streak. This design encouraged deliberate practice, reduced the penalty of early mistakes, and reinforced statistical intuition through repetition and immediate feedback.

The course placed strong emphasis on visual communication as a professional skill. Statistical images, graphs, and charts were uplifted by the media team to improve clarity, consistency, and accessibility, supporting students to interpret outputs and explain findings in plain language. Where possible, interactive assets developed in earlier University of Adelaide courses were reused and adapted, accelerating production while maintaining quality and consistency. Reusing proven components reduced cognitive friction for students, as interactions and visual conventions remained familiar across topics.

Overall, the learning experience combined progressive conceptual scaffolding, purposeful interactivity, and high-quality visual design to build statistical reasoning, critical interpretation, and confident communication in an online environment.