Part B: Course Detail

Teaching Period: Term1 2025

Course Code: MATH5355C

Course Title: Analyse big data

Important Information:

Please note that this course may have compulsory in-person attendance requirements for some teaching activities. 

To participate in any RMIT course in-person activities or assessment, you will need to comply with RMIT vaccination requirements which are applicable during the duration of the course. This RMIT requirement includes being vaccinated against COVID-19 or holding a valid medical exemption. 

Please read this RMIT Enrolment Procedure as it has important information regarding COVID vaccination and your study at RMIT: https://policies.rmit.edu.au/document/view.php?id=209. 

Please read the Student website for additional requirements of in-person attendance: https://www.rmit.edu.au/covid/coming-to-campus 


Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance. 

School: 525T Business & Enterprise

Campus: City Campus

Program: C5404 - Diploma of Marketing and Communication

Course Contact: Nick Reynolds

Course Contact Phone: +61 3 9925 0791

Course Contact Email: nick.reynolds@rmit.edu.au


Name and Contact Details of All Other Relevant Staff

Ryan Gunasekera

ryan.gunasekera@rmit.edu.au

Nominal Hours: 40

Regardless of the mode of delivery, represent a guide to the relative teaching time and student effort required to successfully achieve a particular competency/module. This may include not only scheduled classes or workplace visits but also the amount of effort required to undertake, evaluate and complete all assessment requirements, including any non-classroom activities.

Pre-requisites and Co-requisites

None

Course Description

This unit describes the skills and knowledge required to analyse transactional and non-transactional big data in order to provide insights that are used in an organisation. It involves identifying trends and relationships within big data, and establishing data acceptability. It also involves forming recommendations based on the analysis, and reporting on analysis findings.

It applies to those who work in a broad range of industries and job roles using big data analysis techniques in their day-to-day work.


National Codes, Titles, Elements and Performance Criteria

National Element Code & Title:

BSBXBD403 Analyse big data

Element:

1. Determine purpose and scope of big data analysis

Performance Criteria:

1.1 Determine organisational requirements for big data analysis

1.2 Identify internal and external sources of big data to be analysed according to organisational policies and procedures and legislative requirements

1.3 Establish and confirm parameters to be applied in analysis according to organisational policies and procedures

Element:

2. Analyse initial trends and relationships in captured big data

Performance Criteria:

2.1 Categorise and prepare captured big data for analysis
2.2 Extract and transform structured and unstructured big data in preparation for data analysis

2.3 Analyse big data and derive insights into trends using required tools and dashboards

Element:

3. Finalise big data analysis

Performance Criteria:

3.1 Conduct statistical analysis to confirm accuracy of big data analysis
3.2 Isolate and remove identified incorrect results
3.3 Develop report on key outcomes from analysis
3.4 Store analytics results, associated report and supporting evidence according to organisational policies and procedures, and legislative requirements

Learning Outcomes


This course is structured to provide students with the optimum learning experience in order to demonstrate the skills and knowledge required to analyse transactional and non-transactional big data in order to provide insights that are used in an organisation.


Details of Learning Activities

This unit describes the skills and knowledge required to analyse transactional and non-transactional big data in order to provide insights that are used in an organisation. It involves identifying trends and relationships within big data, and establishing data acceptability. It also involves forming recommendations based on the analysis, and reporting on analysis findings.

It applies to those who work in a broad range of industries and job roles using big data analysis techniques in their day-to-day work.


Teaching Schedule

Week 

Topic Overview and Learning Outcomes

Assessments Due

Week 1

  • Introduction to the course

    Introduction to big Data

    • Staff/student introduction
    • Overview of course
    • Plagiarism, student code of conduct
    • Student support services
    • Course outline and lesson structure
    • How to engage with teacher & fellow students
    • Assessments
  

Week 2
  • Sources of Data
  

Week 3
  • Regulations and Policies
  

Week 4
  • Types of Analysis

 

Week 5
  • SQL
 In Class Assessment 1

Week 6
  • Statistical Analysis

 

Week 7
  • Excel

 

Week 8

  • Assessment Workshop

Assessment 2: Excel Component
Due: 

 

Week 9
  • Tableau
  

Week 10
  • Big Data and AI
  

Week 11
  • Steps of Analysis

 

 

Week 12
  • Presentation of Findings

 

Week 13
  • Assessment Workshop

 

 

Week 14
  • Case Study

 

Week 15

  • Assessment Task Three presentation session

  • MUST ATTEND

 

Assessment 3 Due

Week 16

  • Assessment Task Three presentation session

  • MUST ATTEND

 

 

Week 17-18

Assessment Re-submissions & Resit

Grade entry

 


Learning Resources

Prescribed Texts


References


Other Resources

Available on Canvas


Overview of Assessment

Assessment Methods

Assessment methods have been designed to measure achievement of the requirements in a flexible manner over a range of assessment tasks, for example:

  • direct questioning combined with review of portfolios of evidence and third party workplace reports of on-the-job performance by the candidate
  • review of final printed documents
  • demonstration of techniques
  • observation of presentations
  • oral or written questioning to assess knowledge of software applications

You are advised that you are likely to be asked to personally demonstrate your assessment work to your teacher to ensure that the relevant competency standards are being met.


Performance Evidence

The candidate must demonstrate the ability to complete the tasks outlined in the elements, performance criteria and foundation skills of this unit, including evidence of the ability to:

  • analyse trends and relationships in two different sets of big data: one transactional and one non-transactional
  • report on the results and insights from each analysis
  • store analytics results from each of the two big data sets according to organisational policies and procedures.


Knowledge Evidence

The candidate must be able to demonstrate knowledge to complete the tasks outlined in the elements, performance criteria and foundation skills of this unit, including knowledge of:

  • purpose and benefits to organisation of big data analysis
  • legislative requirements relating to analysing big data, including data protection and privacy laws and regulations
  • organisational policies and procedures relating to analysing big data, including for:
  • identifying big data sources
  • establishing and confirming categories to be applied in analysis
  • analysing data to identify business insights
  • integrating big data sources, including structured, semi-structured, and unstructured
  • combining external big data sources, such as social media, with in-house big data
  • reporting on analysis of big data, including the use of suitable reporting and business intelligence (BI) tools
  • industry protocols and procedures required to write basic queries to search combined big data
  • required analytical techniques and tools to analyse transactional and non-transactional big data, including:
  • data mining
  • ad hoc queries
  • operational and real-time business intelligence
  • text analysis
  • statistical concepts relating to big data analytics
  • relationship between raw big data and big datasets
  • common models and tools to analyse big data, including features and functions of Excel software for advanced analytics of external big data
  • sources of uncertainty within big data
  • classification categories of analytics, including text, audio/video, web and network
  • role of technology and automation tools in performing big data analytics.


Feedback

Feedback will be provided throughout the semester in class and/or online discussions. You are encouraged to ask and answer questions during class time and online sessions so that you can obtain feedback on your understanding of the concepts and issues being discussed. Finally, you can email or arrange an appointment with your teacher to gain more feedback on your progress.

You should take note of all feedback received and use this information to improve your learning outcomes and final performance in the course.


Assessment Tasks

Assessment Task 1 - Knowledge Quiz -

All 21 short answer questions must be answered correctly for you to be assessed as satisfactory for this assessment task. You have two (2) hours to complete this assessment.    

 

Assessment Task 2 - 

This assessment task is second of three assessments for this unit. You will need to complete all three assessments satisfactorily
to be deemed competent for BSBXBD403 Analyse Big Data.
This assessment task will assess your skills and knowledge in analysing big data; structured transactional and unstructured
transactional. Students will prepare data for analysis, extract and transform the data and then analyse it and report on trends
and insights.

 

Assessment Task 3 - 

This assessment task is the third of three assessments for this unit. You will need to complete all three
assessments satisfactorily to be deemed competent for BSBXBD403 Analyse Big Data.
This assessment task will assess your skills and knowledge in analysing big data; structured transactional and unstructured
transactional. You will prepare data for analysis, extract and transform the data and then analyse it and report on trends and
insights.


Assessment Matrix

Available on Canvas

Other Information

None

Course Overview: Access Course Overview