Data Science & Analytics (MSc)

1. Study
2. Postgraduate
3. Taught Courses
4. Masters
5. Data Science and Analytics

About This Course

Explore This Section

Fact File

• Title

  Data Science & Analytics

• Code

  MSCDSA

• College

  Science, Engineering and Food Science

• Duration

  1 Year Full-time

• Teaching Mode

  Full-time

• Qualifications

  MSc

• EU Fees

  €7,130
  See Fees and Costs for full details.

• Non-EU Fees

  €18,900

• Entry Requirements

  See Requirements for full details.

• Closing Date

  Open for EU applications, check rounds closing under How to Apply

• Non-EU Closing Date

  1 December 2022

• Start Date

  11 September 2023

Course Outline

Our MSc in Data Science & Analytics, jointly offered by the School of Computer Science and Information Technology and the Department of Statistics, provides an education in the key principles of this rapidly expanding area. The combination of sophisticated computing and statistics modules will develop your skills in database management, programming, summarisation, modelling, data visualisation, and interpretation of data. The programme provides graduates with an opportunity, through the development of a research project, to investigate the more applied elements of the disciplines.

Students must attain 90 credits through a combination of core modules (30 credits), elective modules (30 credits) and a dissertation (30 credits). 

PART I (60 credits)

Core Modules (30 credits)

• CS6405 Datamining (5 credits) 
• CS6421 Deep Learning (5 credits) 
• ST6030 Foundations of Statistical Data Analytics (10 credits) 
• ST6033 Generalised Linear Modelling Techniques (5 credits)

Database Modules

Students who have adequate database experience take:

• CS6408 Database Technology (5 credits) 

Students who have not studied databases take:

• CS6503 Introduction to Relational Databases (5 credits) 

Elective Modules (30 credits) 

Students must take at least 10 credits of CS (Computer Science) modules and at least 10 credits of ST (Statistics) modules from those listed below:

• CS6322 Optimisation (5 credits) 
• CS6409 Information Storage and Retrieval (5 credits) 
• CS6420 Topics in Artificial Intelligence (5 credits) Semester 1
• CS6426  Data Visualization for Analytics Applications (5 credits) 
• ST6034 Multivariate Methods for Data Analysis (10 credits)
• ST6035 Operations Research (5 credits)
• ST6036 Stochastic Decision Science (5 credits)
• ST6040 Machine Learning and Statistical Analytics I (5 credits)
• ST6041 Machine Learning and Statistical Analytics II (5 credits)

Programming Modules

Students who have adequate programming experience take:

• CS6422 Complex Systems Development (5 credits) 
• CS6423 Scalable Computing for Data Analytics (5 credits) 

Students who have not studied programming take:

• CS6506 Programming in Python (5 credits) 
• CS6507 Programming in Python with Data Science Applications (5 credits) 

All selections are subject to the approval of the programme coordinator.

PART II (30 credits)

• CS6500 Dissertation in Data Analytics (30 credits) or
• ST6090 Dissertation in Data Analytics (30 credits)

See the University Calendar (MSc Data Science & Analytics) for further course and module content.

Postgraduate Diploma in Data Science & Analytics
Students who pass each of the taught modules may opt to exit the programme and be conferred with a Postgraduate Diploma in Data Science & Analytics.

Course Practicalities

A typical 5-credit module entails:

• 2 lecture hours per week;
• 1–2 hours of practicals per week;
• and outside of these regular hours, students are required to study independently by reading and by working in the laboratories and on exercises.

Why Choose This Course

This programme entails a significant collaboration between the School of Computer Science and Information Technology, and the Department of Statistics. It is designed to provide graduates with the skills and knowledge required to help companies and public bodies deal with ever-increasing and complex data.  We emphasise the application of Computer Science and Statistics methodologies helping transform data into useful information that can support decision making. 

Skills and Careers Information

Our MSc programme aims to prepare students to manage, analyse and interpret large heterogeneous data sources. Our graduates will design, compare and select appropriate data analytic techniques, using software tools for data storage/management and analysis, machine learning, as well as probabilistic and statistical methods. Such abilities are at the core of companies that constantly face the need to deal with large data sets.

Companies currently seeking graduates with data analytics skills include: firms specialising in analytics, financial services and consulting, or governmental agencies.

Companies actively recruiting our graduates:

Accenture, Aer Lingus, Agility M3, Allied Irish Banks, Altada Technology Solutions Ltd, Amazon, Apple, Bank of America Merrill Lynch, Bank of Ireland, BT, Central Statistics Office, Cisco, CiTi-Technology, Clearstream, Cloudreach, Dell EMC, Deloitte, Deutche Bank, Enterprise Ireland, Ericsson, Ernst & Young, Ervia, Facebook, First Derivatives, Google, Guidewire, Intel, IBM, Janssen, KPMG, Logitech, Microsoft, Open Text, Paddy Power, Pfizer, Pilz, PWC, SAP Galway, Screendragon, Transverse Technologies, Trend Micro, Tyco, Uniwink, Verizon Connect,  Snipp Interactive, Version 1 (Software), Virgin One, VMware and more.

Starting salaries

There is an increasing demand for graduates that can collate, interpret, manage and store large volumes of data. Graduates can be employed as analysts, database administrators, data warehouse consultants, business intelligent consultants to name but a few. Employment agencies report typical salaries ranging from €45,000-€95,000 depending on industry and experience.

Salaries are in general higher than many other industries; see the Brightwater Salary Survey 2020 for an overview of salaries across relevant industries.

Requirements

Candidates must have:

1. Second Class Honours Grade I in a primary honours degree (NFQ, Level 8) in computer science or mathematical sciences or
2. Second Class Honours Grade I in a primary honours degree (NFQ, Level 8) 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 Second Class Honours Grade II in appropriate modules.

Applicants who do not meet the above standard entry requirements will also be considered under Recognition of Prior Learning (RPL) if they have an undergraduate degree (NFQ, Level 8) and a minimum of 5 years of verifiable relevant industrial experience.

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

Candidates from Grandes Écoles Colleges are also eligible to apply if they are studying a cognate discipline in an ENSEA or EFREI Graduate School and are eligible to enter the final year (M2) of their programme.

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

Non EU applicants, who are required to present an English language proficiency test, must present the certificate on submission of initial application in order for the application to be considered. 

Fees and Costs

The EU fee for this course is €7,130.

The Non-EU fee for this course is €18,900.

How Do I Apply

The closing date for non-EU applications is 1 December 2022

Our MSc in Data Science & Analytics, jointly offered by the School of Computer Science and Information Technology and the Department of Statistics, provides an education in the key principles of this rapidly expanding area. The combination of sophisticated computing and statistics modules will develop your skills in database management, programming, summarisation, modelling, data visualisation, and interpretation of data. The programme provides graduates with an opportunity, through the development of a research project, to investigate the more applied elements of the disciplines.

Students must attain 90 credits through a combination of core modules (30 credits), elective modules (30 credits) and a dissertation (30 credits). 

PART I (60 credits)

Core Modules (30 credits)

• CS6405 Datamining (5 credits) 
• CS6421 Deep Learning (5 credits) 
• ST6030 Foundations of Statistical Data Analytics (10 credits) 
• ST6033 Generalised Linear Modelling Techniques (5 credits)

Database Modules

Students who have adequate database experience take:

• CS6408 Database Technology (5 credits) 

Students who have not studied databases take:

• CS6503 Introduction to Relational Databases (5 credits) 

Elective Modules (30 credits) 

Students must take at least 10 credits of CS (Computer Science) modules and at least 10 credits of ST (Statistics) modules from those listed below:

• CS6322 Optimisation (5 credits) 
• CS6409 Information Storage and Retrieval (5 credits) 
• CS6420 Topics in Artificial Intelligence (5 credits) Semester 1
• CS6426  Data Visualization for Analytics Applications (5 credits) 
• ST6034 Multivariate Methods for Data Analysis (10 credits)
• ST6035 Operations Research (5 credits)
• ST6036 Stochastic Decision Science (5 credits)
• ST6040 Machine Learning and Statistical Analytics I (5 credits)
• ST6041 Machine Learning and Statistical Analytics II (5 credits)

Programming Modules

Students who have adequate programming experience take:

• CS6422 Complex Systems Development (5 credits) 
• CS6423 Scalable Computing for Data Analytics (5 credits) 

Students who have not studied programming take:

• CS6506 Programming in Python (5 credits) 
• CS6507 Programming in Python with Data Science Applications (5 credits) 

All selections are subject to the approval of the programme coordinator.

PART II (30 credits)

• CS6500 Dissertation in Data Analytics (30 credits) or
• ST6090 Dissertation in Data Analytics (30 credits)

See the University Calendar (MSc Data Science & Analytics) for further course and module content.

Postgraduate Diploma in Data Science & Analytics
Students who pass each of the taught modules may opt to exit the programme and be conferred with a Postgraduate Diploma in Data Science & Analytics.

1. Study
2. Postgraduate
3. Taught Courses
4. Masters
5. Data Science and Analytics

About This Course

Explore This Section

Fact File

• Title

  Data Science & Analytics

• Code

  MSCDSA

• College

  Science, Engineering and Food Science

• Duration

  1 Year Full-time

• Teaching Mode

  Full-time

• Qualifications

  MSc

• EU Fees

  €7,130
  See Fees and Costs for full details.

• Non-EU Fees

  €18,900

• Entry Requirements

  See Requirements for full details.

• Closing Date

  Open for EU applications, check rounds closing under How to Apply

• Non-EU Closing Date

  1 December 2022

• Start Date

  11 September 2023

Course Outline

Our MSc in Data Science & Analytics, jointly offered by the School of Computer Science and Information Technology and the Department of Statistics, provides an education in the key principles of this rapidly expanding area. The combination of sophisticated computing and statistics modules will develop your skills in database management, programming, summarisation, modelling, data visualisation, and interpretation of data. The programme provides graduates with an opportunity, through the development of a research project, to investigate the more applied elements of the disciplines.

Students must attain 90 credits through a combination of core modules (30 credits), elective modules (30 credits) and a dissertation (30 credits). 

PART I (60 credits)

Core Modules (30 credits)

• CS6405 Datamining (5 credits) 
• CS6421 Deep Learning (5 credits) 
• ST6030 Foundations of Statistical Data Analytics (10 credits) 
• ST6033 Generalised Linear Modelling Techniques (5 credits)

Database Modules

Students who have adequate database experience take:

• CS6408 Database Technology (5 credits) 

Students who have not studied databases take:

• CS6503 Introduction to Relational Databases (5 credits) 

Elective Modules (30 credits) 

Students must take at least 10 credits of CS (Computer Science) modules and at least 10 credits of ST (Statistics) modules from those listed below:

• CS6322 Optimisation (5 credits) 
• CS6409 Information Storage and Retrieval (5 credits) 
• CS6420 Topics in Artificial Intelligence (5 credits) Semester 1
• CS6426  Data Visualization for Analytics Applications (5 credits) 
• ST6034 Multivariate Methods for Data Analysis (10 credits)
• ST6035 Operations Research (5 credits)
• ST6036 Stochastic Decision Science (5 credits)
• ST6040 Machine Learning and Statistical Analytics I (5 credits)
• ST6041 Machine Learning and Statistical Analytics II (5 credits)

Programming Modules

Students who have adequate programming experience take:

• CS6422 Complex Systems Development (5 credits) 
• CS6423 Scalable Computing for Data Analytics (5 credits) 

Students who have not studied programming take:

• CS6506 Programming in Python (5 credits) 
• CS6507 Programming in Python with Data Science Applications (5 credits) 

All selections are subject to the approval of the programme coordinator.

PART II (30 credits)

• CS6500 Dissertation in Data Analytics (30 credits) or
• ST6090 Dissertation in Data Analytics (30 credits)

See the University Calendar (MSc Data Science & Analytics) for further course and module content.

Postgraduate Diploma in Data Science & Analytics
Students who pass each of the taught modules may opt to exit the programme and be conferred with a Postgraduate Diploma in Data Science & Analytics.

Course Practicalities

A typical 5-credit module entails:

• 2 lecture hours per week;
• 1–2 hours of practicals per week;
• and outside of these regular hours, students are required to study independently by reading and by working in the laboratories and on exercises.

Why Choose This Course

This programme entails a significant collaboration between the School of Computer Science and Information Technology, and the Department of Statistics. It is designed to provide graduates with the skills and knowledge required to help companies and public bodies deal with ever-increasing and complex data.  We emphasise the application of Computer Science and Statistics methodologies helping transform data into useful information that can support decision making. 

Skills and Careers Information

Our MSc programme aims to prepare students to manage, analyse and interpret large heterogeneous data sources. Our graduates will design, compare and select appropriate data analytic techniques, using software tools for data storage/management and analysis, machine learning, as well as probabilistic and statistical methods. Such abilities are at the core of companies that constantly face the need to deal with large data sets.

Companies currently seeking graduates with data analytics skills include: firms specialising in analytics, financial services and consulting, or governmental agencies.

Companies actively recruiting our graduates:

Accenture, Aer Lingus, Agility M3, Allied Irish Banks, Altada Technology Solutions Ltd, Amazon, Apple, Bank of America Merrill Lynch, Bank of Ireland, BT, Central Statistics Office, Cisco, CiTi-Technology, Clearstream, Cloudreach, Dell EMC, Deloitte, Deutche Bank, Enterprise Ireland, Ericsson, Ernst & Young, Ervia, Facebook, First Derivatives, Google, Guidewire, Intel, IBM, Janssen, KPMG, Logitech, Microsoft, Open Text, Paddy Power, Pfizer, Pilz, PWC, SAP Galway, Screendragon, Transverse Technologies, Trend Micro, Tyco, Uniwink, Verizon Connect,  Snipp Interactive, Version 1 (Software), Virgin One, VMware and more.

Starting salaries

There is an increasing demand for graduates that can collate, interpret, manage and store large volumes of data. Graduates can be employed as analysts, database administrators, data warehouse consultants, business intelligent consultants to name but a few. Employment agencies report typical salaries ranging from €45,000-€95,000 depending on industry and experience.

Salaries are in general higher than many other industries; see the Brightwater Salary Survey 2020 for an overview of salaries across relevant industries.

Requirements

Candidates must have:

1. Second Class Honours Grade I in a primary honours degree (NFQ, Level 8) in computer science or mathematical sciences or
2. Second Class Honours Grade I in a primary honours degree (NFQ, Level 8) 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 Second Class Honours Grade II in appropriate modules.

Applicants who do not meet the above standard entry requirements will also be considered under Recognition of Prior Learning (RPL) if they have an undergraduate degree (NFQ, Level 8) and a minimum of 5 years of verifiable relevant industrial experience.

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

Candidates from Grandes Écoles Colleges are also eligible to apply if they are studying a cognate discipline in an ENSEA or EFREI Graduate School and are eligible to enter the final year (M2) of their programme.

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

Non EU applicants, who are required to present an English language proficiency test, must present the certificate on submission of initial application in order for the application to be considered. 

Fees and Costs

The EU fee for this course is €7,130.

The Non-EU fee for this course is €18,900.

How Do I Apply

The closing date for non-EU applications is 1 December 2022

Please note you will be required to provide additional information as part of the online application process for this programme. This will include the following questions:

• You may enter the details of professional or voluntary positions held. We strongly encourage you to complete this section with all relevant work experiences that will support your application.

• In addition to your previously declared qualifications, please outline any additional academic courses, self-learning and professional training relevant to this programme.

• Please describe your motivation and readiness for this programme.