Part B: Course Detail

Teaching Period: Term2 2022

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

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 course is structured to provide you with the optimum learning experience. A range of learning activities are provided during the semester and are designed to enhance learning and understanding of the topics.

You will be required to participate in a combination of group and individual learning activities. These activities will be provided through classroom work time and additional learning activities will be provided to you to complete outside of the scheduled class time.

A range of in class activities, case studies and independent research is included as the learning activities for this course. We expect you to participate and contribute in all scheduled learning activities.

The learning activities will also include group discussion, group problem solving activities and opportunities to practice your skills in a simulated workplace environment.


Teaching Schedule

 

Course Schedule:                                                           Semester 2: 2022

Week

Week Commencing

Topic / Activities

(including any pre-reading, research and resources required)

Assessment

Week 1

 4th July 2022

Introduction to Big Data

  • Introduction to Unit and teacher
  • Unit guide – handout and discussion
  • Elements of Competency – What will the unit cover
  • Overview of Assessments 1 – 3
  • Subject protocols – methods of delivery/ facilitation
  • Required materials
  • Communication 

 

Week 2

11th July 2022

Sources of Data

 

Week 3

18th July 2022

Regulations and Policies

 

Week 4

25th July 2022

Types of Analysis

 

Week 5

1st August 2022

SQL

In Class Assessment 1: Short Answers Test Week beginning 1sth August 2022

 

 

Week 6

8th August 2022

Statistical Analysis

 

Week 7

15th August 2022

Excel

 

Week 8

 22nd August 2022

Assessment Workshop

  

Week 9

5th September 2022

Tableau

Assessment 2: Excel Component
Due: 11th September

Week 10

12th September 2022

Presentation of Findings

 

Week 11

 19th September 2022

Steps of Analysis

 

Week 12

26th September 2022

Databases

 

Week 13

3rd October 2022

 Assessment Workshop

 

Week 14

10th October 2022

 Case Study

 

Week 15

17th October 2022

Course Revision

 

Week 16

 24th October 2022

Re-submissions and resit 

Assessment  task 3: Analyse Big Data and Report  
Due: 30th October

Week 17

31st October 2022

Grade Finalisation

 


Learning Resources

Prescribed Texts


References


Other Resources

All resources will be available in Canvas.  Tableau access will be provided.


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 1

Summary and Purpose of Assessment

This assessment task is first of three assessments for this unit. You will need to complete all three assessments satisfactorily to be deemed competent for BSBXBD403 Analyse Big Data.  

 

The purpose of this assessment is to assess your knowledge on Big Data analysis

 

Assessment Instructions

What

You are required to answer 21 short answer questions about big data. This is an open book assessment.

 

Where

This assessment will be completed in class.

 

How

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.   

  • This is an individual assessment to be completed in class time.
  • You must not copy the work of others. (For more information regarding Academic Integrity please refer to RMIT Academic Integrity Guidelines.)
  • This is intended as a written assessment. You are to make arrangements with the assessor as soon as possible if you are eligible for special allowance or allowable adjustment to this assessment (e.g. verbal assessment or additional time).
  • You can seek clarification or guidance from the assessor about this assessment task.
  • You will be assessed as satisfactory or not satisfactory.
  • Please refer to the Course Guide for information regarding re-submissions.
  • You will have the opportunity to resubmit any tools that are deemed unsatisfactory (one resubmit allowed).

You  can appeal the assessment decision according to the RMIT Assessment Processes 

Assessment 2

Summary and Purpose of Assessment

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 3

Summary and Purpose of Assessment
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

The assessment matrix that maps all the assessment is available on CANVAS.

 

Submission Requirements

 

You should:

  • Ensure that you submit assessments on or before the due date.  
  • Always retain a copy of your assessment tasks. (hard copy and soft copy)
  • When you submit work for assessment at RMIT University you need to use the Assessment task document that includes a declaration and statement of authorship.
  • Each page of your assessment should include footer with your name, student number, the title of the assessment, unit code and title and page numbers.

Other Information

Late Submission Procedures  

You are required to submit assessment items and/or ensure performance based assessment is completed by the due dates. 

If you are prevented from submitting an assessment item on time, by circumstances outside your control, you may apply in advance to your teacher for an extension to the due date of up to seven calendar days.

 

More Information:  https://www.rmit.edu.au/students/student-essentials/assessment-and-exams/assessment/extensions-of-time-for-submission-of-assessable-work

 

Where an extension of greater than seven days is needed, you must apply for Special Consideration.  Applications for special consideration must be submitted no later than two working days after the assessment task deadline or scheduled examination.

 

More Information: https://www.rmit.edu.au/students/student-essentials/assessment-and-exams/assessment/special-consideration

 

Resubmissions: 

If you are found to be unsuccessful in a particular Course Assessment Task (or you do not submit/attend) you will be allowed one resubmission.  Your teacher will provide feedback regarding what you need to do to improve and will set a new deadline for the resubmission.  

 

If you are still not meeting the assessment requirements you must apply to your Program Manager in writing outlining the steps you will take to demonstrate competence in your course. Your submission will be considered by the Program Team and you will be advised of the outcome as soon as possible.

 

 

Adjustments to Assessment  

In certain circumstances students may be eligible for an assessment adjustment. For more information about the circumstances under which the assessment arrangements might be granted please access the following website: 

https://www.rmit.edu.au/students/student-essentials/assessment-and-exams/assessment/adjustments-to-assessment

 

Marking Guide (Competency): 

Feedback on your assignment and your results will be released via the rubric on Canvas. Assessment tasks will receive the following outcomes:

Satisfactory

Not Satisfactory

DNS (Did not Submit)

There are 3 assessments for this course, students must be deemed satisfactory i n all 3 assessments to be competent in this course.

Course grades will be given as:

CA (Competency Achieved)

NYC (Not Yet Competent)

DNS (Did not submit)

 

You must demonstrate that you have all the required skills/knowledge/elements in the unit of competency you are studying. 

You will receive feedback on each assessment task that will inform you about your progress and how well you are performing.  

Further information regarding the application of the grading criteria will be provided by your teacher.

Course Overview: Access Course Overview