Skip to content Accessibility statement
Student home
HomeStudent homeStudying at YorkManage your studiesProgrammes and modulesHeader - DPLv2

Cloud Based Data Analysis - COM00207M

«Back to module search

  • Department: Computer Science
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2026-27
    • See module specification for other years: 2025-26

Module will run

Occurrence Teaching period
A Semester 2 2026-27

Module aims

The aim of this module is to allow students to develop an understanding of cloud computing and utilising cloud-based services for diverse applications such as data analytics, scalable machine learning or edge computing. The students will learn the principles and the state-of-the-art of large-scale cloud computing for data-intensive applications. Alongside, they will also learn about the data ecosystem, including databases, data warehouses, data marts, data lakes and data pipelines. In continuation with these, they will explore the state-of-the-art big data and cloud technologies and use them for data analysis, preparation and pipeline development for robust machine learning and deep learning applications with modern infrastructure. Through practical exercises and laboratory sessions, students will gain hands-on experience with various cloud computing tools, enabling them to manage and deploy applications on cloud-based platforms. The learning outcomes will provide the students a foundation to understand cloud platform services and the high-performance requirements of large-scale data analysis, and enable them to develop robust advanced analytics applications utilising the cloud platform services.

Module learning outcomes

  • Articulate and analyse the cloud computing concepts, including the principles, benefits, and associated challenges

  • Critically discuss the data ecosystem

  • Thoroughly analyse the high-performance requirements of big data analysis

  • Implement, evaluate, deploy, and monitor machine learning applications and big data analytics pipelines using cloud based resources

  • Develop advanced analytics services provided by the cloud based on user/organisational requirements

Module content

Operational concerns

As this is a new module covering cloud computing and data analytics, academic staff from different expertise may be involved in preparing the detailed learning resources.

Industrial Focus

Students will gain a deep understanding of how cloud technologies are applied in specific industries. They will be better equipped to address industry challenges and meet the demands of employers in sectors where cloud computing plays a pivotal role through several ways, including:

  • industry-specific case studies and examples throughout the cloud computing module to help students understand real-world applications of cloud technologies in different sectors.

  • working with real-world data

  • collaborating with industry experts (inviting external speakers, working alongside industry)

Indicative assessment

Task % of module mark
Essay/coursework 100.0

Special assessment rules

None

Indicative reassessment

Task % of module mark
Essay/coursework 100.0

Module feedback

-

Indicative reading

Data Engineering with Databricks Cookbook: Build effective data and AI solutions using Apache Spark, Databricks, and Delta Lake

  • Author:Pulkit Chadha
  • Publisher:Packt Publishing
  • ISBN-13:978-1837633357

Visual Analytics with Tableau

  • Authors:Alexander Loth, Nate Vogel, Sophie Sparkes
  • Publisher:Wiley
  • Publication Date:May 31, 2019
  • ISBN-13:978-1119560203

Learning Tableau 2025: Leverage Tableau's newest features to revolutionize your data storytelling with AI-enhanced insights

  • Author:Joshua N. Milligan
  • Publisher:Packt Publishing
  • Publication Date:August 8, 2025
  • ISBN-13:978-1835886786

Mastering Microsoft Azure Fundamentals: AZ-900 Exam Prep

  • Author:Jakub Osowski
  • Publisher:Independently published
  • Publication Date:February 16, 2025
  • ISBN-13:979-8311854887

Mastering Azure Data Factory for Modern Data Integration: Design, Automate and Build Real-Time Data Integration Pipelines and BI Solutions(Azure Data Engineer — Expert Path)

  • Authors:Ashish Agarwal, Narendra Ragho Angane, Vijay Kumar
  • Publisher:Orange Education Pvt Ltd
  • Publication Date:July 3, 2025
  • ISBN-13:978-9349888814

Azure Data Factory Cookbook: Build ETL, Hybrid ETL, and ELT pipelines using ADF, Synapse Analytics, Fabric and Databricks

  • Authors:Dmitry Foshin, Tonya Chernyshova, Dmitry Anoshin, Xenia Ireton
  • Publisher:Packt Publishing
  • Publication Date:February 28, 2024
  • ISBN-13:978-1803246598


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.