Course Title: Applied Analytics

Part A: Course Overview

Course Title: Applied Analytics

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH1324

City Campus

Postgraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 1 2007,
Sem 1 2008,
Sem 1 2009,
Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 2 2014,
Sem 1 2015,
Sem 2 2015,
Sem 1 2016,
Sem 2 2016

MATH1324

City Campus

Postgraduate

171H School of Science

Face-to-Face

Sem 1 2017,
Sem 2 2017,
Sem 1 2018,
Sem 2 2018,
Sem 1 2019,
Sem 2 2019,
Sem 1 2020,
Sem 2 2020,
Sem 1 2021,
Sem 2 2021

Flexible Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2406

RMIT Online

Postgraduate

171H School of Science

Internet

JanJun2020 (KP1)

MATH2406

RMIT Online

Postgraduate

171H School of Science

Internet

JulDec2020 (TP5)

Course Coordinator: Laleh Tafakori

Course Coordinator Phone: +61 (03) 9925 2589

Course Coordinator Email: laleh.tafakori@rmit.edu.au

Course Coordinator Location: 15.4.8

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

A working knowledge of basic mathematics and familiarity with computers.


Course Description

This course will introduce you to fundamental statistical concepts and modern statistical practice, as used in analysis. You will study statistical data investigations, summary statistics, data visualisation and probability as a measure for uncertainty. You will then build upon these topics and learn about sampling, sampling distributions and confidence intervals as the basis for statistical inference, and decision making. The course will finish with a series of modules looking at common hypothesis testing methods for different types of data. There is an emphasis on conceptual understanding, interpretation of statistical output and the use of statistical technology, namely R, for statistical computation in an analytic or data science context


Objectives/Learning Outcomes/Capability Development

 This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

Personal and professional awareness

  • the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • the ability to reflect on experience and improve your own future practice
  • the ability to apply the principles of lifelong learning to any new challenge.

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.

Problem-solving

  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.


On completion of this course you will achieve the following Course Learning Outcomes (CLO):

  1. Plan a statistical data investigation by selecting the appropriate approach to solve the problem, considering a range of analytical approaches including the issues and pitfalls in applying these techniques and biases introduced through data collection
  2. Use relevant open-source environments and tools (e.g. R) to perform fundamental statistical analyses (descriptive analysis, hypothesis testing, ANOVA, correlation and linear regression) and support communication and visualisation of key results.
  3. Communicate results accurately and in a way to prevent or minimise potential bias, errors in sampling data 


Overview of Learning Activities

This course uses highly structured learning activities to guide your learning process and prepare you for your assessments. The activities are a combination of individual, peer-supported and facilitator-guided activities, and where possible project-led, with opportunities for feedback throughout.  

Authentic and industry-relevant learning is critical to this course and you will be encouraged to practise on real world problems. Social learning is another important component and you are expected to participate in class and group activities, share drafts of work and resources and give and receive peer feedback. You will be expected to work efficiently and effectively with others to achieve outcomes greater than those that you might have achieved alone.  

Above all, the learning activities are designed to maximise the likelihood that you will not only understand the course learning resources but also apply that learning to improving your own practice, for example by producing real-world artefacts and engaging in scenarios and case studies.  


Overview of Learning Resources

There are no prescribed texts for this course. All course content, notes and learning materials will be available through the course website. 

This course will use the statistical software package R and the RStudio integrated development environment. R and RStudio are free. Students will require R and RStudio installed on their personal computing device. Student will be notified of ways to access this software on campus computers and through online services. 

http://rmit.libguides.com/mathstats 


Overview of Assessment

This course has no hurdle requirements. 

  

Assessment Tasks 

Assessment Task 1:   Practical assessments 

Weighting 30% 

This assessment task supports CLOs 1, 2 and 3. 

  

Assessment Task 2: Module exercises 

Weighting 20% 

This assessment task supports CLO 1, 2 and 3 

  

Assessment Task 3:   Online formative assessments 

Weighting 50% 

This assessment task supports CLOs 1, 2 and 3