General Information
Building on students’ existing knowledge of data science techniques, this subject investigates the range of deployment options to automatically extract insights from the vast amount of data available. This includes traditional server and database deployment, as well as a range of popular cloud solutions including open-source alternatives. The advantages and disadvantages of different approaches will be discussed. In addition to popular big data analytics deployment options such as Amazon Web Services (AWS), Microsoft Azure, Google Big Query, Apache Spark, H20.ai and NoSQL, students will also learn about the MapReduce and Hadoop framework. Importantly, security implications associated with big data analytic deployments will be discussed, including knowledge of principles for cybersecurity and an ability to implement basic best practices.
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Details
Academic unit: Bond Business School Subject code: DTSC13-300 Subject title: Infrastructure for Data Analytics Subject level: Undergraduate Semester/Year: January 2023 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Forum: x12 (Total hours: 24) - Forum 1
- Computer Lab: x12 (Total hours: 24) - Computer Lab 2
- Personal Study Hours: x12 (Total hours: 72) - Recommended study time & reviewing materials
Attendance and learning activities: Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. -
Resources
Prescribed resources: No Prescribed resources.
After enrolment, students can check the Books and Tools area in iLearn for the full Resource List.iLearn@Bond & Email: iLearn@Bond is the online learning environment at Bond University and is used to provide access to subject materials, lecture recordings and detailed subject information regarding the subject curriculum, assessment and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student. To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au
Academic unit: | Bond Business School |
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Subject code: | DTSC13-300 |
Subject title: | Infrastructure for Data Analytics |
Subject level: | Undergraduate |
Semester/Year: | January 2023 |
Credit points: | 10.000 |
Timetable: | https://bond.edu.au/timetable |
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Delivery mode: | Standard |
Workload items: |
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Attendance and learning activities: | Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. |
Prescribed resources: | No Prescribed resources. After enrolment, students can check the Books and Tools area in iLearn for the full Resource List. |
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iLearn@Bond & Email: | iLearn@Bond is the online learning environment at Bond University and is used to provide access to subject materials, lecture recordings and detailed subject information regarding the subject curriculum, assessment and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student. To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au |
Enrolment requirements
Requisites: |
Nil |
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Assumed knowledge: |
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge. Assumed Prior Learning (or equivalent):
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Restrictions: |
Nil |
Assurance of learning
Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.
At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.
Program Learning Outcomes (PLOs)
Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.
Subject Learning Outcomes (SLOs)
On successful completion of this subject the learner will be able to:
- Identify and apply frameworks for distributed storage and parallel processing using multiple (virtual) computers
- Describe a variety of cloud-based deployment options for big data analytics, and an ability to implement simple, prototype deployments
- Identify a variety of traditional database and server deployment options for big data analytics and implement simple, prototype deployments
- Articulate the security risks, particularly cybercrime associated with a variety of deployment options for big data analytics
- Identify the principles of cyber-safe deployment and implement basic safeguards to prototype deployments
- Critically compare the advantages and disadvantages of different deployment options for big data analytics
Generative Artificial Intelligence in Assessment
The University acknowledges that Generative Artificial Intelligence (Gen-AI) tools are an important facet of contemporary life. Their use in assessment is considered in line with students’ development of the skills and knowledge which demonstrate learning outcomes and underpin study and career success. Instructions on the use of Gen-AI are given for each assessment task; it is your responsibility to adhere to these instructions.
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Assessment details
Type Task % Timing* Outcomes assessed Written Report Report 40% Week 10 3,4,5,6 Skills Test Skills test 40% Week 12 1,2,3,4,5,6 Student Engagement Participation 20% Ongoing 2,4 - * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
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Assessment criteria
Assessment criteria
High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment. Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Type | Task | % | Timing* | Outcomes assessed |
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Written Report | Report | 40% | Week 10 | 3,4,5,6 |
Skills Test | Skills test | 40% | Week 12 | 1,2,3,4,5,6 |
Student Engagement | Participation | 20% | Ongoing | 2,4 |
- * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
Assessment criteria
High Distinction | 85-100 | Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. |
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Distinction | 75-84 | Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. |
Credit | 65-74 | Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. |
Pass | 50-64 | Usually awarded to students whose performance meets the requirements set for work provided for assessment. |
Fail | 0-49 | Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. |
Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Study Information
Submission procedures
Students must check the iLearn@Bond subject site for detailed assessment information and submission procedures.
Policy on late submission and extensions
A late penalty will be applied to all overdue assessment tasks unless an extension is granted by the subject coordinator. The standard penalty will be 10% of marks awarded to that assessment per day late with no assessment to be accepted seven days after the due date. Where a student is granted an extension, the penalty of 10% per day late starts from the new due date.
Academic Integrity
The University’s Academic Integrity Policy defines plagiarism as the act of misrepresenting as one’s own original work: another’s ideas, interpretations, words, or creative works; and/or one’s own previous ideas, interpretations, words, or creative work without acknowledging that it was used previously (i.e., self-plagiarism). The University considers the act of plagiarising to be a breach of the Student Conduct Code and, therefore, subject to the Discipline Regulations which provide for a range of penalties including the reduction of marks or grades, fines and suspension from the University.
Bond University utilises Originality Reporting software to inform academic integrity.Feedback on assessment
Feedback on assessment will be provided to students within two weeks of the assessment submission due date, as per the Assessment Policy.
Accessibility and Inclusion Support
If you have a disability, illness, injury or health condition that impacts your capacity to complete studies, exams or assessment tasks, it is important you let us know your special requirements, early in the semester. Students will need to make an application for support and submit it with recent, comprehensive documentation at an appointment with a Disability Officer. Students with a disability are encouraged to contact the Disability Office at the earliest possible time, to meet staff and learn about the services available to meet your specific needs. Please note that late notification or failure to disclose your disability can be to your disadvantage as the University cannot guarantee support under such circumstances.
Additional subject information
As part of the requirements for Business School quality accreditation, the Bond Business School employs an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Subject curriculum
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Introduction to data analytic Infrastructure with an information systems approach
Data analytic systems are now used as a standard part of business operations. These systems require appropriate infrastructure to operate correctly and efficiently. We define infrastructure categories using a classical information systems approach.
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Data: Database implementations and frameworks, and a history of data storage
Paper and electronic databases have been an important part of businesses for many decades. The rise of data analytics has meant that traditional relational approaches need to be supplemented by emerging NoSQL style databases. These appropriately capture increasingly important semi-structured and non-structured data.
SLOs included
- Identify a variety of traditional database and server deployment options for big data analytics and implement simple, prototype deployments
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Hardware, Software: Cloud based solutions and a survey of applications
The cloud gives public and company accessibility to a large amount of hardware and software infrastructure. The key issues including types of resources and security issues are discussed.
SLOs included
- Describe a variety of cloud-based deployment options for big data analytics, and an ability to implement simple, prototype deployments
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Hardware: Computing methods and different types of hardware
The base of any digital information system is hardware. The five different categories of hardware are discussed in relation to the needs of data analytic systems.
SLOs included
- Critically compare the advantages and disadvantages of different deployment options for big data analytics
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People and Procedures: Security and encryption
One of the largest issues in any digital system, including data analytic systems, is security. Common security threats as well as measures to protect sensitive data are discussed.
SLOs included
- Articulate the security risks, particularly cybercrime associated with a variety of deployment options for big data analytics
- Identify the principles of cyber-safe deployment and implement basic safeguards to prototype deployments
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Software: Parallel computing and big data tools
Given the growth of data that requires analysis, parallel resources are needed to process data, particularly for real time applications and continuous data. Paradigms such as MapReduce and tools like Hadoop and Spark are explored. Both coarse and fine grain parallelism techniques and metrics are given.
SLOs included
- Identify and apply frameworks for distributed storage and parallel processing using multiple (virtual) computers
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People and Procedures: Different job types and roles in big data in organisations and government
People are the most important part of any system. The organisation of people, in either traditional company structures and emerging flat and matrix structures, is discussed along with the advantages and disadvantages of each. The different job roles in data analytic system ecosystems are also explored.
SLOs included
- Critically compare the advantages and disadvantages of different deployment options for big data analytics
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People and Procedures: Algorithms, pseudocode and business processes
The documentation of data analytic systems is incredibly important to ensure that users and developers have an accurate understanding of them. Three different aspects of this topic, algorithms, pseudocode, and business processes are explored. The tools of efficiency analysis and business process modelling notation are introduced.
SLOs included
- Critically compare the advantages and disadvantages of different deployment options for big data analytics
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Data: Blockchains
Blockchains are now being used to securely store and manipulate continuous streams of data. Topics such as their use in digital currency and data mining are explored.
SLOs included
- Identify and apply frameworks for distributed storage and parallel processing using multiple (virtual) computers
- Critically compare the advantages and disadvantages of different deployment options for big data analytics
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People and Procedures: Privacy and ethics
In data driven systems, breaches of privacy and unethical use of data, are important considerations. Both potential threats and the design of mitigation measures are discussed. Different ethical frameworks are also considered.
SLOs included
- Critically compare the advantages and disadvantages of different deployment options for big data analytics
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Emerging Trends and subject summary
Data analytic systems rapidly change over time given advances in technology. New technologies relevant to future infrastructure needs are considered. Additionally, a summary of all aspects of infrastructure are covered in this final topic.
SLOs included
- Identify and apply frameworks for distributed storage and parallel processing using multiple (virtual) computers
- Describe a variety of cloud-based deployment options for big data analytics, and an ability to implement simple, prototype deployments
- Identify a variety of traditional database and server deployment options for big data analytics and implement simple, prototype deployments
- Articulate the security risks, particularly cybercrime associated with a variety of deployment options for big data analytics
- Identify the principles of cyber-safe deployment and implement basic safeguards to prototype deployments
- Critically compare the advantages and disadvantages of different deployment options for big data analytics