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MA5820 - Statistical Methods for Data Scientists

Credit points: 3
Year: 2019
Student Contribution Band: Band 2
Administered by: College of Science and Engineering

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 misinterpretting results can lead to false discoveries and managers making poor and costly decisions. To avoid this, it is very important that students learn the best ways to present and analyse data. This subject will introduce students to practical applications and concepts involved in descriptive and 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 computational tool of choice.

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 in a form suitable for specialist and non-specialist audiences.

Availabilities

External, Study Period 86
Census Date 07-Nov-2019
Lecturer: Dr Carla Ewels.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 65 hours - Online resources including readings, screencasts, embedded quizzing
  • assessment and self-directed study
Method of Delivery: WWW - LearnJCU
Assessment: quizzes or tests (20%); assignments (60%); reports/oral presentation (20%).

External, Study Period 82
Census Date 21-Mar-2019
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 65 hours - Online resources including readings, screencasts, embedded quizzing
  • assessment and self-directed study
Method of Delivery: WWW - LearnJCU
Assessment: quizzes or tests (20%); assignments (60%); reports/oral presentation (20%).

JCU Online, External, Study Period 82
Census Date 21-Mar-2019
Coordinator: Assoc. Professor Yvette Everingham
Lecturer: Dr Carla Ewels.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 65 hours - Online resources including readings, screencasts, embedded quizzing
  • assessment and self-directed study
Method of Delivery: Online - JCU
Assessment: quizzes or tests (20%); assignments (60%); reports/oral presentation (20%).

JCU Online, External, Study Period 86
Census Date 07-Nov-2019
Coord/Lect: Dr Carla Ewels.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 65 hours - Online resources including readings, screencasts, embedded quizzing
  • assessment and self-directed study
Method of Delivery: Online - JCU
Assessment: quizzes or tests (20%); assignments (60%); reports/oral presentation (20%).

Note: Minor variations might occur due to the continuous Subject quality improvement process, and in case of minor variation(s) in assessment details, the Subject Outline represents the latest official information.