Semester: 2
ECTS: 4
Lectures: 30
Practice sessions: 30
Independent work: 60
Module Code: 23-02-539
Semester: 2
ECTS: 4
Lectures: 30
Practice sessions: 30
Independent work: 60
Module Code: 23-02-539

Module title:


Data analytics in cloud computing


Module overview:


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.


Literature:


Essential reading:
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

Recommended reading:
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

Further reading:
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