Master Advanced Computing

+ Information by E-mail

Master Advanced Computing

  • Objectives Data mining is finding increasing acceptance in science and business areas which need to analyse large amounts of data to discover trends and recognise patterns that they could not otherwise find. IT spending for data mining has been robust in recent years. This specialist course is designed for those who wish to pursue a career in the emerging and exciting area of data mining and data warehousing. It is likely to be of interest to graduates in database and computing, or related subjects or those who have been working in IT industry and would like to develop further down a route that involves handling and manipulating data. The MSc aims to provide you with the knowledge and skills to prepare you to work in application development or associated commercial activities, or to progress to further research in the field.
  • Entry requirements The standard entry requirement is a lower second-class Honours degree including units in both database and computing. We will also consider applicants who have relevant professional qualifications or substantial relevant professional experience in an IT environment. Only applicants with programming skills may be admitted on the course. For overseas applicants, the standard entry requirement is a 4-year undergraduate degree or equivalent OR a postgraduate degree from a government approved or accredited institute or University with an overall mark of 65% or higher (or equivalent grade) in Computer Science, Computer Science and Engineering, Computer Applications, Information Systems Design or similar. Overseas applicants must also hold a minimum score of 1400 (Quantitative 550; Verbal 350 and Analytical 500) or above in the Graduate Record Examinations (GRE) or other equivalent. If English is not the applicant's first language, we require an IELTS score of 6.0 or above, a TOEFL score above 600, or a TOEFL on-line test score of 250 or above
  • Academic title MSc Advanced Computing: Data Mining and Warehousing
  • Course description Course structure
    The course consists of six essential modules and a dissertation of 15,000 words. The core modules include:

    -Datamining and Data Warehousing
    -Large Scale Object-Oriented Database Software Development
    -Research Methods and Project Preparation
    -Statistics for Datamining
    -Intelligent Techniques for Datamining and Data Warehousing
    -Web Mining and Web Search

    Assessment
    A range of methods are used including individual and group exercises and examinations.

    Career opportunities
    On completion of the course you will be a well-honed IT professional ready to join a datamining and data warehousing team. You would be able to make an immediate contribution, with minimum training, in the areas of bioinformatics, pharmaceutics, financial services, marketing information services, health, telecommunications, text mining and web mining. You would have particular competencies in the architectural and contextual issues relating to datamining and data warehousing technologies. This course also equips you for pursuing further academic study such as a PhD.

    Attendance & duration
    Full-time: 12 months, three days a week

    Part-time (day): 2 years, two and a half days a week
+ Information by E-mail

Other programs related to computer science

  • Advanced Computing Master

  • Institution: King's College London
  • + Information by E-mail
  • Master Computing & Internet Systems

  • Institution: King's College London
  • + Information by E-mail
  • Master Advanced Computing

  • Institution: King's College London
  • + Information by E-mail
  • Advanced Information Systems (MSc)

  • Institution: Birkbeck, University of London
  • + Information by E-mail
  • Computer Science and Information Systems (PhD - MPhil)

  • Institution: Birkbeck, University of London
  • + Information by E-mail
  • Computer Science (MRes)

  • Institution: Birkbeck, University of London
  • + Information by E-mail
  • Master Advanced Methods in Computer Science

  • Institution: Queen Mary, University of London
  • + Information by E-mail