Cloud Computing for Data Science and AI (University of Suffolk)

Module info

Module Title	Cloud Computing for Data Science and AI
Module Code	IPLDSAM03
Level	7 (MSc / postgraduate)
School	School of Technology, Business and Arts
Github Repo	UoS_CloudComputing

Offered at

About this module

The on-demand delivery of compute, database, storage, applications and IT resources through cloud computing has enabled many organisations to deliver innovative solutions without upfront capital investment. Cloud computing ecosystems provide a variety of scalable AI and machine learning solutions. This module provides a comprehensive grounding in cloud computing concepts and solutions, buttressed with extensive practicals to build experience in individual services and architectural designs. As the University of Suffolk is an AWS Academy partner institution, the module will give you an opportunity to acquire AWS certification(s) if you so wish.

The aims of the module are:

• to introduce the concept of cloud computing as it applies to Data Science and Artificial intelligence, its advantages and limitations, economics, services and architectures
• to introduce cloud computing architecture concepts such as client, application, server, Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Data as a service (DaaS)
• to introduce different types of cloud computing services deployment (Public Cloud, Private Cloud, Hybrid Cloud, Community Cloud)
• to introduce issues around cloud computing security and privacy, replication, reliability;
• to introduce different cloud business models and applications for Data Science and AI (Amazon AWS, Microsoft Azure, Google Cloud);
• to provide hands on practice with actual commercial cloud services - students will be able to design a cloud solution using commercial cloud services

Learning Outcomes

On successful completion of this module, a student will be able to:

1. Evaluate the technical dimensions of AI and data science architectures and solutions with specific emphasis on cloud approaches
2. Combine theory, research and practice at forefront of the discipline and use it to guide the development of robust and high quality cloud solutions and architectures
3. Critically review cloud solutions and tools for AI and data science problems
4. Demonstrate system-level competencies in assessing, understanding, creating and improving cloud-based architectural solutions

Module outline

Session	Topic	Lecture	Practical/Lab	Additional Digital Self-paced Training	Knowledge check
Lecture 1	Introduction to Cloud computing, Data Science and AI
Lecture 2	Cloud Concepts I: architecture, infrastructure, services, best practices
Lecture 3	Cloud Concepts II: security, privacy, replication, reliability, compliance
Lecture 4	Data Science in the Cloud I: collecting & storing data
Lecture 5	Data Science in the Cloud II: Database services - Querying your data
Lecture 6	Data Science in the Cloud III: Processing & cleaning Data
Lecture 7	Data Science in the Cloud IV: Analysis and Visualisation
Lecture 8	Data Science in the Cloud V: ecosystem comparisons (Amazon AWS, Microsoft Azure, Google Cloud)
Lecture 9	Artificial Intelligence: How cloud computing changed everything
Lecture 10	Cloud Computing Architecture: Migrating a data analytics project to the cloud I
Lecture 11	Cloud Computing Architecture: Migrating a data analytics project to the cloud II
Lecture 12	Module Recap and Assignment Q&A

Lecture 1

Introduction to Cloud computing, Data Science and AI

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