Semester: 3
ECTS: 4
Lectures: 30
Practice sessions: 30
Independent work: 60
Module Code: 23-02-542
Semester: 3
ECTS: 4
Lectures: 30
Practice sessions: 30
Independent work: 60
Module Code: 23-02-542

Module title:


Big data analysis techniques


Module overview:


The objectives of this module are to enable students to:
• Critically evaluate and apply big data techniques using software such as Python
• Interpret a systematic approach in order to build and apply skills in big data network analytics, text mining, and social media data mining
• Demonstrate critical awareness of how managers and executives utilise big data analytics for business value creation by improving their operational, social, and financial performance and create opportunities for new business development.

Students will learn to adopt approaches which unlock the potential of large data sets using analytical tools and techniques to plan business activities. The aim of the module is to enable students to actively use tools and analytical techniques that will help them to extract knowledge from large data sets for business planning purposes.

It is important for students to take this module in order to develop theoretical understanding of big data analytics including the effective use of big data concepts and their links with big data analytics.

Example applications of big data analytics discussed within the module will focus on addressing contemporary challenges faced by industry. Students will also learn practical skills and managerial insights through guided demonstrations involving a variety of exercises that will prepare them to be data-driven managers and executives capable of utilising big data analytics for business value creation.


Literature:


Essential reading:
1. Bird,S., Klein,E., and Loper,E. (2009). Natural Language Processing with Python, Sebastopol, Massachusetts: O’Reilly
2. Klepac, G. (2014) Data Mining Models as a Tool for Churn Reduction and Custom Product Development in Telecommunication Industries, in Vasant P. (Ed.), Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications (pp. 511-537). Hershey, PA: Information Science Reference. doi:10.4018/978-1-4666-4450-2.ch017, Hershey, USA
3. Miner, G. (2012) Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, Oxford: Oxford, Academic Press

Recommended reading:
1. Almeida, F., and Santos, M. (2014) A Conceptual Framework for Big Data Analysis, in Portela I. and Almeida F. (Eds.) Organizational, Legal, and Technological Dimensions of Information System Administration (pp. 199-223). Hershey, PA: Information Science Reference. doi:10.4018/978-1-4666-4526-4.ch011, Hershey, USA
2. Bakshi, K. (2014) Technologies for Big Data, in W. Hu, and N. Kaabouch (Eds.) Big Data Management, Technologies, and Applications (pp. 1-22). Hershey, PA: Information Science Reference. doi:10.4018/978-1-4666-4699-5.ch001, Hershey, USA
3. Bird,S., Klein,E., and Loper,E. (2009) Natural Language Processing with Python, Sebastopol, Massachusetts: O’Reilly
4. Cointet, J. P., and Roth, C. (2009) Socio-semantic dynamics in a blog network. International Conference on Computational Science and Engineering, doi:10.1109/CSE.2009.105, Paris, France
5. Conte, R., Gilbert, N., Bonelli, G., Helbing, D. (2011) FuturICT and social sciences: Big Data, big thinking, Zeitschrift für Soziologie, 40, 412–413., Zurich, Switzerland