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# MA5820 - Statistical Methods for Data Scientists

 Credit points: 3 Year: 2023 Student Contribution Band: Band 1 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.

### Subject Assessment

• Written > Test/Quiz 1 - (10%) - Individual
• Written > Problem task - (40%) - Individual
• Written > Project report - (50%) - Individual.

## Availabilities

Cairns, Study Period 86, Internal
Census Date 09-Nov-2023
Coordinator: Dr David Donald

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

• 26 hours tutorials
• assessment and self-directed study
Restrictions: Enrolment in this offering is restricted.

JCU Brisbane, Trimester 1, Internal
Census Date 09-Mar-2023

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

• 65 hours was Other - Online resources including readings, screencasts, embedded quizzing.
• assessment and self-directed study

JCU Brisbane, Trimester 2, Internal
Census Date 22-Jun-2023

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

• 65 hours was Other - Online resources including readings, screencasts, embedded quizzing.
• assessment and self-directed study

JCU Brisbane, Trimester 3, Internal
Census Date 05-Oct-2023

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

• 65 hours was Other - Online resources including readings, screencasts, embedded quizzing.
• assessment and self-directed study

JCU Online, Study Period 82, External
Census Date 23-Mar-2023
Coordinator: Dr David Donald

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

• 65 hours was Other - Online resources including readings, screencasts, embedded quizzing.
• assessment and self-directed study
Method of Delivery: Online - JCU

JCU Online, Study Period 86, External
Census Date 09-Nov-2023