CS989 – Big Data Fundamentals

TIMETABLE TEACHING MATERIAL
Credits 10
Level 5
Semester 1
Prerequisites N/A
Availability Possible elective
Contact Lectures: 10 | Labs: 10
Homework/Private Study: Assignments: 80
Assessment The class is assessed 100% by coursework.
Resit TBC
Lecturer Dr Martin Halvey

General Aims

The aim of this module is to endow students with:

  • an understanding of the new challenges posed by the advent for big data, as they refer to its modelling, storage, and access;
  • an understanding of the key algorithms and techniques which are embodied in data analytics solutions.

Learning Outcomes

After completing this module participants will be able to:

  • understand the fundamentals of Python to enable the use of various big data technologies;
  • understand how classical statistical techniques are applied in modern data analysis;
  • understand the potential application of data analysis tools for various problems and appreciate their limitations.

Syllabus

  • Introduction to Python;
  • Introduction to R objects, data types and descriptive statistics;
  • Quantitative methods for data analysis and knowledge extraction including classification and regression, clustering, association rules, Bayesian approaches, decision trees.

Recommended Text/Reading*

* This list is indicative only – the class lecturer may recommend alternative reading material. Please do not purchase any of the reading material listed below until you have confirmed with the class lecturer that it will be used for this class.

Learning Python. Lutz, M. O’Reilly Media Inc. 2013. | Stocked at Amazon (Other retailers are available)

R Manuals at http://www.r-project.org