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Data Science and Analytics MSc - CKR49 full time. CKR50 part time

Fact File

Course Title: Data Science and Analytics

College: Science Engineering and Food Science

Data Science and Analytics

Duration: 1 year Full Time or 2 years Part Time

Teaching Mode: Full-time, Part-Time

Qualifications: MSc

NFQ Level: Level 9

Costs: 2014/2015 Irish/EU €5,500

Entry Requirements: See detailed entry requirements

Course Code: CKR49 full time. CKR50 part time

Closing Date: This programme is closed for applications for 2014

Next Intake: 8th September 2014

Overview

This programme of the Department of Computer Science and the School of Mathematical Sciences provides an education in the key principles of the rapidly-developing area of Data Science and Analytics.  In addition to the basic computational underpinnings of this field, a sequence of courses in probability and statistics will develop skills in analysis, summarisation and modelling of data.

The programme also allows graduates an opportunity, through development of a research portfolio, to investigate the more applied elements of the discipline. At all times the programme stresses the importance of data science, statistics and probability theory as key tools in the analysis of large-scale heterogeneous data.

Companies are currently seeking graduates with data analytics skills to fill a range of positions including firms specialising in analytics, financial services and consulting, and governmental agencies.

Below please find link to brochure for further information see our brochure at  http://www.ucc.ie/en/media/academic/computerscience/documents/coursedescriptions/MScDataScienceAnalytics..pdf

Course Details

Students take 90 credits as follows:

Students must attain ninety credits through a combination of core modules (30 credits) and elective modules (30 credits) in semesters 1 & 2 and a dissertation (30 credits) during the summer period.

Core Modules (30 credits)

Students must take the database sequence, and the courses ST6030, ST6033 and CS6405

  • ST6030 (Foundations of Statistical Data Analytics) [10 credits]
  • ST6033 (Generalised Linear Modelling Techniques) [5 credits]
  • CS6405 (Data Mining) [5 credits]


Database Sequence Modules*:

  • Introductory: CS6503 (Introduction to Relational Databases) [5 credits] plus CS6505 (Database Design and Administration) [5 credits]   OR
  • Advanced:  CS6408 Database Technology (5 credits) plus CS6409 Information Storage and Retrieval (5 credits)

*If students have adequate prior database experience, they are required to take a more advanced database sequence (CS6408 and CS6409) in place of the introductory database modules (CS6503 and CS6505).
All selections are subject to approval of the programme coordinator.
 
Elective Modules
(30 credits)
Students must take at least 10 credits of Computer Science (CS) modules and at least 10 credits of Statistics (ST) modules from those courses listed below.
CS6322  Optimisation (5 credits)
CS6323  Analysis of Networks and Complex Systems (5 credits)
CS6506
Programming in Python (5 credits) or CS6406 Large-Scale Application Development and Integration 1 (5 credits)
CS6507 Programming in Python with Data Science Applications (5 credits) or
CS6407 Large-Scale Application Development and Integration 2 (5 credits)
CS6509  Internet Computing for Data Science (5 credits)
ST6032  Stochastic Modelling Techniques (5 credits)
ST6034  Multivariate Methods for Data Analysis (10 credits)
ST6035  Operations Research (5 credits)
ST6036  Stochastic Decision Science (5 credits)
Students can choose elective programming courses based on their prior training: if students have adequate prior programming experience, they are required to take a more advanced programming sequence (CS6406 and CS6407) in place of the introductory programming modules (CS6503 and CS6505).
All selections are subject to approval of the programme coordinator.
 

Project Phase (After Semester 2)
(30 credits)
Students select one of the following modules:
CS6500  Dissertation in Data Analytics (30 credits)
ST6090  Dissertation in Data Analytics (30 credits)

Detailed Entry Requirements

  1. obtained either a second class honours level 8 primary degree in computer science or mathematical sciences or
  2. a second class honours level 8 primary degree with a strong numerate content (e.g. engineering, finance, physics, biosciences or economics).  In such cases the programme team must be satisfied that the numerate content is sufficient for entry to the programme and that applicants have an aggregate grade of a 2H2 in appropriate modules.

Applicants who do not meet the above standard entry requirements will also be considered if they have an undergraduate degree (at Level 7 or > Level 8) and a minimum of 5 years verifiable relevant industrial experience.

Applicants who do not have a primary degree will only be considered with a minimum of 10 years verifiable relevant industrial experience.

Shortlisted applicants who do not meet the standard entry requirements will be invited for interview.

Application Procedure

Application for this programme is on-line at www.pac.ie/ucc. Places on this programme are offered in rounds. The closing dates for each round can be found here. For full details of the application procedure click How to Apply.

All required documentation must be either uploaded to your online application, or sent in hard copy to The Postgraduate Applications Centre, 1, Courthouse Square, Galway, immediately after an application is made.

Course Practicalities

A typical 5 credit module:

•             2 lecture hours per week

•             1–2 hours of practicals per week

•             Outside these regular hours students are required to study independently by reading and by working in the laboratories and on exercises.

Assessment

The MSc in Data Science & Analytics will be examined through a combination of:

  • Mid-term exams
  • End-of-term exams
  • Assignments

Dissertation

Further Contact Information

Dr Michael Cronin, Department of Statistics

E: M.cronin@ucc.ie

T: +353 (0)21 420 5825

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