Big Data and Cloud Computing

Teaching Hours and Credit Allocation: 30 Hours, 6 Credits
Course Assessment: Exam & Coursework

 

Aims

The big data explosion has led to new computing paradigms, the most prevalent among them being cloud computing. Cloud computing is about vast computing resources on demand, that allow for centralized data storage and online access. Big data is a broad term that includes several concepts and tasks, such as data capture, storage, sharing, management and analysis. This course focuses mostly on the big data storage and management part, rather than the analysis as well as cloud service models, architectures and tools. Students will familiarize with modern big data and cloud technologies, understand the privacy and security concerns and learn about popular big data and cloud computing platforms.

 

Learning Outcomes

On completing the course students will be able to:

  • Develop the knowledge, understanding and skills to work with Big data
  • Deploy a structured lifecycle approach to data analytics problems
  • Apply appropriate analytic techniques and tools to analyzing big data
  • Understand Cloud Computing Concepts and Mechanisms
  • Learn the concepts, principles, techniques and methodologies you need to manage cloud services and resources

 

Content

  • Big data concepts, principles and practical applications
  • Big data capture, storage, sharing, management and analysis
  • Cloud Computing Concepts and Mechanisms
  • Cloud Architectures
  • Working with Clouds
  • Managing Cloud Services and Resources
  • Big data and cloud computing platforms

 

Reading

T. Erl, R. Puttini, Z. Mahmood, Cloud Computing: Concepts, Technology & Architecture, Pearson, 2013.

EMC Education Services (Editor), Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley 2015.