Mathematics & Statistics - Data & Computational Science

MSc Data & Computational Science
Graduate Taught (level 9 nfq, credits 90)

The MSc in Data and Computational Science is designed for students from highly quantitative disciplines who wish to work in data analytics or computational science.

- The programme will equip you to solve complex scientific problems and analyse large data sets using a range of theoretical tools, from deterministic mathematical modelling to Bayesian analysis.

- The intensive programming modules will allow you develop a range of sought-after skills in practical programming and data analytics, including applications in high-performance computing.

- Topical application areas are offered each year, including cryptography, numerical weather prediction, and financial mathematics. The dissertation will give you further handson experience in computational science and will allow you to apply the key theoretical and practical skills by working on a challenging research topic.

Who should apply?
Full Time option suitable for:

Domestic(EEA) applicants: Yes
International (Non EEA) applicants currently residing outside of the EEA Region. Yes

Part Time option suitable for:
Domestic(EEA) applicants: Yes
International (Non EEA) applicants currently residing outside of the EEA Region. No

The MSc in Data and Computational Science is designed for students from highly quantitative disciplines who wish to work in data analytics or computational science.

Course Description
Computational science is at the crossroads between modern applied mathematics and statistics, and our programme recognizes this fact by combining aspects of both in a unique set of tailored modules including scientific computing, mathematical modelling, and data analytics.

Programme Outcomes
Analyze and interpret data, find patterns and draw conclusions

Apply computationally based techniques to formulate and solve problems

Approach problems in an analytical, precise and rigorous way

Demonstrate an in-depth understanding of the interface of applied mathematics, statistics and computational science.

Demonstrate familiarity with the areas of data and computational science currently under active research

Give oral presentations of technical material at a level appropriate for the audience

Model real-world problems in an applied mathematical or statistical framework

Prepare a written report on technical content in clear and precise language

Undertake excellent research at an appropriate level, including survey and synthesize the known literature

Use the language of logic to reason correctly and make deductions

Work independently and be able to pursue a research agenda

Entry requirements

This programme is intended for applicants who have an Upper Second class honours degree or higher, or the international equivalent, in a highly quantitative subject such as Mathematics, Physics, Statistics, Engineering.

Applicants whose first language is not English must also demonstrate English language proficiency of IELTS 6.5 (no band less than 6.0 in each element), or equivalent.
School of Mathematics and Statistics Application Process FAQ

These are the minimum entry requirements – additional criteria may be requested for some programmes

Subjects taught

Core modules in simulation and modelling:

Simulation Modelling and Analysis
C and Fortran programming
Parallel computing using MPI
Mathematica for Research

Core modules in statistics and data analytics
Linear Models
Statistical Data Mining
Data programming
Multivariate Analysis
Bayesian Analysis

Optional topical modules, for example:
Cryptography and Coding Theory
Advanced Fluid Mechanics
Weather and Climate
Financial Mathematics

Modules and topics shown are subject to change and are not guaranteed by UCD.

Stage 1 - Core
Mathematica for ResearchACM40730
Uncertainty QuantificationACM41000
Applied Matrix TheoryMATH40550
Predictive Analytics ISTAT30240
Statistical Machine LearningSTAT30270
Multivariate AnalysisSTAT40150
Data Programming with RSTAT40620
Data Prog with Python (online)STAT40800
Bayesian Analysis (online)STAT40850
Principles of Prob & StatsSTAT41040

Stage 1 - Option
Advanced Topics in Computational ScienceACM40080
Numerical AlgorithmsACM40290
High Performance Computing (ICHEC)ACM40640
Scientific Programming Concepts (ICHEC)ACM40660
Research Project IIACM40910
Projects in Maths ModellingACM40960
Advanced Predictive AnalyticsSTAT30250
Monte Carlo InferenceSTAT40400
Data Prog with C (online)STAT40780
Stochastic Models (online)STAT40810
Adv Data Prog with R (online)STAT40830
Data Prog with SAS (online)STAT40840
Adv Bayesian Analysis (online)STAT40950
Stat Network Analysis (online)STAT40960
Machine Learning & AI (online)STAT40970

Duration

1 year full-time.

Post Course Info

Careers & Employability
The unique combination of modules and skills offered by this programme will equip graduates to work in a range of specific sectors in data analytics, data science, quantitative modelling in finance, and computational science and engineering. This is a new highly specialized programme in the school; recent past graduates from similar programmes in the school work in firms including:

• ICT companies (e.g. Google, Paddy Power, LinkedIn)
• The financial services industry (e.g. Citi, Deloitte, Geneva Trading, Murex)

More details
  • Qualification letters

    MSc

  • Qualifications

    Degree - Masters (Level 9 NFQ)

  • Attendance type

    Full time,Daytime

  • Apply to

    Course provider