- Class 30
- Practice 30
- Independent work 60
Data analytics in cloud computing
The objectives of this module are to enable students to learn to:
• Interpret fundamental ideas behind cloud computing, the evolution of the paradigm, its applicability benefits, as well as current and future challenges;
• Interpret basic ideas and principles in data centre design; cloud management techniques and cloud analytics deployment considerations
• Evaluate the economics of cloud computing
• Accurately evaluate distributed computing challenges and opportunities and apply this knowledge to real-world projects
• Introduce students with cloud analytic concepts and general insights into analytical services in the cloud, including also 3 major market players (IBM, Oracle and Microsoft).
In this module students will learn how to manage data analytics and cloud computing to help direct business strategy to optimize resources and maximize profits. Ideally data analytics
helps eliminate much of the guesswork involved in trying to understand clients, instead systemically tracking data patterns to best construct business tactics and operations to minimize uncertainty. Not only does analytics determine what might attract new customers,
often analytics recognizes existing patterns in data to help better serve existing customers, which is typically more cost effective than establishing new business.
It is important for students to take this module to be able to manage ever-changing business world subject to countless variants in cloud empowered analytics solutions. Analytics gives companies the edge in recognizing changing climates so they can take initiate appropriate
action to stay competitive. Alongside analytics, cloud computing is also helping make business more effective and the consolidation of both clouds and analytics could help businesses store, interpret, and process their big data to better meet their clients’ needs.
1. Onn, Y. (2005) Privacy in the Digital Environment, Haifa: Haifa Center of Law and Technology
2. Hager, G., Wellein, G. (2010) Introduction to High Performance Computing for Scientists and Engineers, https://www.amazon.de/Introduction-Performance-Computing-Scientists-Computational/dp/143981192X, available at pdf: https://pdfs.semanticscholar.org/d45e/c41b45caa8686fa1788d9191ab4044a18a83.pdf
1. Maheshwari, A. (2016) Big Data Essentials, Seattle: Amazon Digital Services LLC
2. Marz, N. (2015) Big Data analytics, New York: Manning Publications
3. Eijkhout, V. (2015) Introduction to High Performance Scientific Computing,(https://www.amazon.com/Introduction-High-Performance-Scientific-Computing/dp/1257992546), available at http://pages.tacc.utexas.edu/~eijkhout/Articles/EijkhoutIntroToHPC.pdf
1. Gebali, F. (2013) Algorithms and Parallel Computing, https://www.amazon.com/Algorithms-Parallel-Computing-Fayez-Gebali/dp/0470902108), available at https://aicitel.files.wordpress.com/2013/02/parallel-algorithms.pdf
Minimal learning outcomes
- Evaluate the role of analytics systems in the cloud and the main advantages and disadvantages over traditional "on premise" systems.
- Explain the technologies on which cloud analytics is based and design the process and prerequisites for migrating an existing or implementing a new cloud analytics system.
- Identify most common cloud analytics platforms, their components and describe key differences between them.
- Identify most common cloud based cognitive services, their components and describe key differences between them.
Preferred learning outcomes
- Define steps in migration plan for cloud analytics implementation process together with key milestones.
- Select most suitable technologies and evaluate cost per key resources for cloud analytics system adoption for selected case (migration of existing/new development).
- Apply a cloud analytics tool to analyze data on a real-world example and your own data set.
- Analyze and apply cognitive services as part of data science solution in the cloud, including infrastructure planning and value proposition for selected case.