MA5820 - Statistical Methods for Data Scientists
Credit points: |
3 |
Year: |
2021 |
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
|
External,
Study Period 82
|
Census Date 18-Mar-2021 |
Lecturer:
|
Dr David Donald. |
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 |
|
|
External,
Study Period 86
|
Census Date 04-Nov-2021 |
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 |
|
|
JCU Online,
External,
Study Period 86
|
Census Date 04-Nov-2021 |
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 |
|
|
JCU Online,
External,
Study Period 82
|
Census Date 18-Mar-2021 |
Lecturer:
|
Dr David Donald. |
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 |
|
|
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.