Programs

Data Science

Analytical techniques based on large data sets

  • Class 30
  • Practice 30
  • Independent work 120
Total 180

Course title

Analytical techniques based on large data sets

Lecture type

Obligatory

Course code

20-02-006

Semester

3

ECTS

6

Lecturers and associates

Course objectives

The aim of the course is to develop students' awareness of the potential of large data sets using analytical tools and techniques to plan business activities. The aim of the course 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.

Content

An introduction to large data sets and their analytics. Python programming language - basics. Data structures in python. Objects in python. Open source libraries for large data sets analysis. Analysis of unstructured data source for the campaign. Understanding the Text. Planning of business activities based on the discovery of quoted samples and meaning. Targeted campaigns and big days. Early warning system development strategy. System development strategies for preventive termination of contractual relations. Strategies for segmentation system development. CRM system development strategies. Integration of technologies. Trends and future.

Required reading

Bird,S., Klein,E., and Loper,E. (2009). Natural Language Processing with Python. Sebastopol,
O’Reilly
Klepac, G. (2014). Data Mining Models as a Tool for Churn Reduction and Custom Product Development in Telecommunication Industries. In P. Vasant (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
Miner, G. (2012). Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, Oxford, Academic Press

Additional reading

Almeida, F., and Santos, M. (2014). A Conceptual Framework for Big Data Analysis. In I. Portela, and F. Almeida (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
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
Bird,S., Klein,E., and Loper,E. (2009). Natural Language Processing with Python. Sebastopol,
O’Reilly
Cointet, J. P., and Roth, C. (2009). Socio-semantic dynamics in a blog network. International Conference onComputational Science and Engineering. doi:10.1109/CSE.2009.105
Conte, R., Gilbert, N., Bonelli, G., and Helbing, D. (2011). FuturICT and social sciences: Big Data, big thinking.
Zeitschrift für Soziologie, 40, 412–413.

Minimal learning outcomes

  • Review potentials of large data sets
  • Review analytical techniques to analyze large data sets
  • Review campaigns using knowledge from large data sets
  • Evaluate product quality using knowledge from large data sets
  • Evaluate tools end techniques for image recognition

Preferred learning outcomes

  • Evaluate the potentials of large data sets for business planning
  • Evaluate analytical techniques to analyze large data sets
  • Recommend methods for discovering knowledge from large data sets for your campaign
  • Recommend methods for discovering knowledge from large data sets for the development of new products
  • Recommend tools and techniques for specific computer vision use-case
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