MIT9314 Advanced Cloud Data Analytics
Unit Code
MIT9314
Level
Postgraduate
Campus
Sydney
Prerequisites
MIT8204
Credit Points
10
Subject Description
MIT9314 Advanced Cloud Data Analytics equips students with advanced skills in harnessing the power of big data for strategic decision-making. Students work with sophisticated analytics tools, cloud-based platforms, and scalable data processing techniques, gaining expertise in leveraging cloud resources for complex data analysis. The subject provides students with hands-on experience in utilising large-scale data processing platform through cloud-based analytics services. Through lectures and case studies students will develop the expertise to design and implement cloud-based data analytics solutions, preparing them for roles in the era of big data.
Course Level
Level 2
Subject Type
☒ Specialisation (MIT Data Science)
☒ Elective (MIT)
Study Mode: Face to face on site
Study Load: Full-time
Learning Topics
- Cloud data analytics architectures and strategic applications
- Cloud data ingestion, streaming and interactive exploration
- Data visualisation and dashboard development
- Scalable machine learning using Spark, BigQuery and TensorFlow
- Real-time analytics across Google Cloud, Amazon and Microsoft ecosystems
Subject Learning Outcomes
- Assess and recommend appropriate frameworks for strategic decision-making using big data in the cloud.
- Analyse and develop cloud-based solutions that respond to organisational data analytics goals.
- Contextualise advanced cloud-based analytics tools and platforms to process, analyse, and visualise large-scale data, ensuring efficiency, scalability, and alignment with organisational decision-making needs.
- Employ modern data analysis skills in designing and implementing machine learning and artificial intelligence solutions within cloud environments.
- Design and deploy scalable data cleaning and data wrangling methods that utilise cloud resources effectively.
Assessment
AIIHE uses a variety of assessment tools to guide and assess each student’s achievement of the subject learning outcomes. In this subject, students undertake portfolio this assessment will evaluate students’ participation and learning in the class, individual case study students will analyse a case study on a business organisation benefiting from data ingestion, preparation, analysis, and visualisation using google cloud, and group report and presentation students will work in groups to complete and present a cloud data analytics project. These tasks assess the knowledge, applied skills, critical analysis, communication and collaborative capabilities relevant to the subject.
Summary of Assessment
- Students must attempt all assessment tasks, and achieve an overall mark of at least 50%, to pass this subject.
- Assessment in this subject is consistent with and informed by the AIIHE Assessment Policy and Procedure.
| Assessment Type | Due Date | Weighting |
|---|---|---|
| Assessment 1: Portfolio | Week 1 to 12 | 30% |
| Assessment 2: Individual Case Study | Week 8 | 30% |
| Assessment 3: Group Report and Presentation | Week 12, 13 | 40% |
Learning Resources
AIIHE will update the topic coverage of the subject and the available relevant learning resources at the time of subject delivery. In the interim, AIIHE provides the following indicative list of relevant reference material. AIIHE also subscribes to eLearning Resources through the Canvas portal, and where relevant material is available, AIIHE will seek to source reference material from this system for staff and student access.
Prescribed Reading
- Lakshimanan, V. (2022). Data Science on the Google Cloud Platform. 2nd edn. USA: O’Reilly.
Texts and References
- Kane, F, Maarek, S. (2023). AWS Certified Data Analytics Specialty Hands-on. USA: Packt.
- Lakshmanan, V., Tigani, J. (2019). Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale. USA: O’Reilly.
Recommended Reading
- Shiyal, B. (2021). Beginning Azure Synapse Analytics: Transition from Data Warehouse to Data Lakehouse. USA: Apress.
- Fregly, C., Barth, A. (2021). Data Science on AWS. USA: O’Reilly.
- Tejada, Z. (2017). Mastering Azure Analytics: Architecting in the Cloud with Azure Data Lake, HDInsight, and Spark. USA: O’Reilly.