Course Title: Practice of Analytics

Part A: Course Overview

Course Title: Practice of Analytics

Credit Points: 12.00

Important Information:

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Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2392

City Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 2 2020

Course Coordinator: Dr. Stelios Georgiou

Course Coordinator Phone: +61 3 9925

Course Coordinator Email: @rmit.edu.au

Course Coordinator Location: 08.09.074

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

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Course Description

Practice of Analytics is a course that aims in developing applied skills using several different statistical software.   This course introduces you to the concepts and the different software, focusing on their differences and their usefulness in relation to the data in consideration.   An introduction to the available statistical software and analytics tools will be provided and practical aspect on data analytics will be applied. 

 


Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for BP083 Bachelor of Applied Mathematics and Statistics:   PLO2. Knowledge and technical competence • The ability to use the appropriate software and modern computational tools to apply basic analytics methodologies   PLO3. Problem-solving • The ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems   

 


On completion of this course you will be able to:   1. Know the available tools for practical analytic and be able to use them effectively. 2. Perform basic statistical analysis using a statistical software. 3. Use a statistical software in an efficient way. 4. Apply known statistical techniques in real data. 5. Develop strategies and using several available analytical tools to inference from data of various types.

 


Overview of Learning Activities

Key concepts of tools available for the practice of Analytics will be extensively covered in this course. These will be explained and elucidated with relevant class and computer laboratory examples. The assignments/labs and project will test your understanding of class materials.


Overview of Learning Resources

A list of prescribed/ recommended textbooks for this course will be provided on Canvas. All course materials will be posted on Canvas. The software packages can be accessed from the school computer labs, as well as through the RMIT MyDesktop system anywhere anytime.   Library Subject Guide for Mathematics & Statistics http://rmit.libguides.com/mathstats    


Overview of Assessment

This course has no hurdle requirements.    Assessment Tasks:Assessment Task 1: Assignments Weighting 15% This assessment task supports CLOs  1, 2, 3, 4 & 5   Assessment Task 2: Lab Assignments - related to usage of computer software Weighting 15% This assessment task supports CLOs  1, 2, 3, 4 & 5   Assessment Task 3: Project Weighting 20% This assessment task supports CLOs  1, 2, 3, 4 & 5   Assessment Task 3: Final Exam Weighting 50% This assessment task supports CLOs  1, 2, 3, 4 & 5