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 and costly decisions. To avoid this, it is very important to be confident with presenting and analysing data using optimal methods and approaches.

This subject will introduce students to practical applications and concepts involved in descriptive statistics, inferential statistics and linear modelling. Topics include methods of producing, exploring, displaying and summarising data, both of single and multiple variables, probability and sampling concepts, confidence intervals, hypothesis testing, correlation and regression. 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 where appropriate.

The subject also runs discussion forums and collaborate sessions. Students are encouraged to participate in the online discussion forum. Discussion boards give you a place to interact with staff and other students about subject content and topics, and help you to clarify and extend your understanding of key content.

While attendance in the Collaborate session is not mandatory, it is highly recommended. These sessions will provide you with the opportunity to have synchronous (at the same time) conversations with the demonstrator and with your fellow students from across the subject.

Software platform: R Studio 

Learn more about JCU's Data Science academics.

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.

 

Learning outcomes

  • Demonstrate 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.

Assessment

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.


This is one of the interdisciplinary subjects studied in the online Master of Data Science

Please note, course structure and content are subject to change. For information on all course subjects download the course guide.

Download Course Guide

 

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