Course Summary
This skillset involves the skills and knowledge required to capture, store, test and analyse transactional and non-transactional big data from a range of sources. It includes using established methodologies and techniques to obtain data sets prior to storing, assembling, processing and identifying key information contextualised to the needs of the audience or work area, according to industry practices, organisational policies, procedures and protocols and providing reporting, recommendations and insights in an appropriate and accessible format. Participants undertaking this skillset are expected to have existing relevant qualifications. No licensing, legislative, regulatory or certification requirements apply to this unit at the time of publication however participants may be subject to restrictions or additional requirements based on operational, site or workplace policies and procedures.
Requirements - To Be Eligible
For your application to be considered, you must meet the following entry requirements.
Entry RequirementsParticipants undertaking this skillset are expected to have existing relevant qualifications. No licensing, legislative, regulatory or certification requirements apply to this unit at the time of publication however participants may be subject to restrictions or additional requirements based on operational, site or workplace policies and procedures.
Course Details
| Course Type | Short Course (Accredited) |
|---|---|
| Student Availability |
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| Fees | For detailed information on Course Fees, visit www.cqu.edu.au/fees |
| Application Mode |
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| How to Apply | Please Enquire |
| Study Area |
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| Skill Area |
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Admission Codes
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Domestic Students Tertiary Admission Centre Codes (TAC) Codes |
Not Applicable |
|---|---|
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International Students CRICOS Codes |
Not Applicable |
Student Outcomes, Career Opportunities and Occupations
This skillset involves the skills and knowledge required to capture, store, test and analyse transactional and non-transactional big data from a range of sources. It includes using established methodologies and techniques to obtain data sets prior to storing, assembling, processing and identifying key information contextualised to the needs of the audience or work area, according to industry practices, organisational policies, procedures and protocols and providing reporting, recommendations and insights in an appropriate and accessible format.
Study Mode Definitions
- Online: Online courses provide the flexibility to study without normally requiring a student to visit a campus. Course content is studied through a number of means including the use of online discussion forums, electronic library resources, by contacting lecturers and teachers, and receiving study materials online/electronically. Work integrated learning, including placements, may be included in some courses.
- On-campus: Students studying in on-campus mode typically are expected to attend and participate in regular, structured on-campus teaching and learning activities throughout the University’s academic term. These activities may include lectures, tutorials, workshops and practice, online or other activities and normally will be timetabled at a CQUniversity campus or approved delivery site.
- Mixed Mode: Students studying in mixed mode will participate in a combination of online learning activities in addition to site-specific learning activities, which may include residential schools, co-op placements and/or work-integrated learning as a compulsory requirement of a unit. The additional site-specific learning activities are what differentiates a mixed-mode unit from an online unit.
*All study modes may include Vocational placement and/or work integrated learning.
Select a course structure below to view the delivery details, including the different study modes offered for each structure.
This skillset includes four units of competency and is typically delivered in a range of modes, including self-directed online, teacher led webinars, and face-to-face workshops to participants with existing industry knowledge.
The duration of the skillset is typically 114 delivery hours.
- RPL/RCC/Credit
- On-campus
- Mixed Mode
- Online
| Unit Group 1 | ||||
|---|---|---|---|---|
| BSBXBD406 | Present big data insights | More Information | Training.gov Details | |
| BSBXBD403 | Analyse big data | More Information | Training.gov Details | |
| BSBXBD402 | Test big data samples | More Information | Training.gov Details | |
| BSBXBD401 | Capture and store big data | More Information | Training.gov Details | |
Unit Sequence
| Unit Code | Unit Name |
|---|---|
| BSBXBD406 | Present big data insights |
| BSBXBD403 | Analyse big data |
| BSBXBD402 | Test big data samples |
| BSBXBD401 | Capture and store big data |