Statistical Methods for Data Science

Subject code: MA5820:03

Statistics is used in many disciplines. Applying statistical methods the right way can help data scientists make new discoveries and help managers make better decisions. Conversely, applying statistical methods inappropriately and misinterpreting results can lead to false discoveries and managers making poor, costly decisions. To avoid this, it is very important that you learn the best ways to present and analyse data.

This subject will introduce you to practical applications and concepts involved in descriptive and inferential statistics, and linear modelling. Emphasis will be placed on communicating findings from data investigations to a range of audiences. RStudio will be the tool of choice. A calculator will also be used to facilitate numerical calculations.

Learn more about JCU's Data Science acdemics.

Video transcript

Hi, my name is Yvette and I'm a statistician in the College of Science and Engineering. I'm dedicated to identifying strategies that would deliver better learning outcomes in STEM education. 

This subject provides students with an introduction to statistics. So, we can think of statistics as being the science of data, and in this subject, we will equip students with the practical skills and know-how to be able to collect, analyse and draw conclusions from data. 

And this is really important in data science, because in today's world, it's very easy to collect data and store data. We have faster computers and all we've ever had. And so, these skills are actually integral to data science.

There are a few key learning outcomes in this subject; one of those outcomes is basic familiarity with statistical methods, but we're also hoping to make students comfortable with working with RStudio which is a software program that will be embedded in the course.

We want students to be able to be confident with the software. We want students to be able to correctly interpret the outputs that are generated from Rstudio. 

Another outcome is the ability to communicate, so embedded in this course is an assessment which will test your ability to communicate findings to an expert and a non-expert audience.


You will explore the following topics:

  • Methods of producing, exploring, displaying and summarising data (single and multiple variables)
  • Probability and sampling concepts
  • Confidence intervals
  • Hypothesis testing
  • Correlation and regression.

Learning outcomes

Students who successfully complete this subject will be able to:

  • demonstrate a sound knowledge of the basic principles that underpin sample selection
  • experimental design, statistical theories, data visualisation and linear modelling
  • effectively integrate and execute statistical theories and processes in RStudio
  • retrieve, analyse, synthesise and evaluate outputs produced from RStudio
  • integrate statistical principles, methods, techniques and tools covered in this course to plan and execute a statistical analysis
  • evaluate, synthesise and communicate findings from statistical investigations in a form suitable for specialist and non-specialist audiences.


Assessment for this course will occur at various times across the seven-week study period. Tasks may include online quizzes, discussion board activity, portfolio development, case studies, reflection, literature reviews presentations and reports.Feedback will be provided to you throughout the study period as well as a final grade at the conclusion of the study period.

Please note, unit structure and content are subject to change. Contact your JCU Online student advisor on 1300 535 919 for more information based on your particular circumstances.


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