Data Science and Analytics Munster Technological University

Overview

The significance of Data Science has surged in recent years, driven by an increasing demand for specialists across diverse sectors. As data generation and collection continue to rise, the necessity for proficient analysis becomes paramount for both public and private organisations striving to remain competitive and leverage data advantages. The Higher Diploma in Science in Data Science and Analytics (NFQ Level 8) at Munster Technological University (MTU) is a collaborative effort between the Department of Mathematics and the Department of Computer Science. This programme is meticulously crafted to cultivate highly skilled graduates ready to excel in the fast-growing domain of Data Science.

Designed in partnership with industry professionals, the curriculum ensures that students acquire vital competencies in programming, database management, statistical modelling, time series analysis, machine learning, as well as data visualisation and interpretation. The course is structured to provide a comprehensive understanding of these core areas, thereby fostering a robust foundation for students.

Course delivery involves a blend of online learning and on-campus attendance at MTU's Bishopstown Campus in Cork for assessments and examinations each semester. This flexible approach allows students to balance their studies with other commitments effectively. The programme is structured to encompass 60 credits, with three main strands—Data Science, Statistics, and Computer Science—being developed over two semesters. As students progress, they will specialise in areas such as statistical analysis and machine learning.

Students will benefit from numerous practical opportunities, applying theoretical knowledge through hands-on laboratory sessions and projects. A capstone project is a vital component of the programme, allowing learners to synthesise their learning and apply it to real-world problems in Data Science and Analytics.

Graduates will gain substantial practical experience with various software tools and programming languages, including R, Python, Excel, SQL, NoSQL, Tableau, Spark, and Hadoop. This extensive exposure ensures that students are well-prepared for the demands of the industry upon graduation.

Why Data Science and Analytics at Munster Technological University?

MTU is renowned for its commitment to delivering high-quality education and fostering strong industry partnerships. The university provides state-of-the-art facilities and resources, ensuring that students have access to the latest technologies and tools necessary for their studies. The collaborative approach of the programme, developed with input from industry experts, enhances the relevance and applicability of the curriculum.

Tuition Fee Breakdown

The tuition fees for the Higher Diploma in Science in Data Science and Analytics are as follows:

• International Fee: €6,300 per full academic year
• European Fee: €6,300 per full academic year

Visit the Fees and Funding section for a breakdown in your local currency.

Syllabus

The programme includes both mandatory and elective modules, ensuring a well-rounded educational experience. The mandatory modules are:

• Intro to R for Data Science
• Data Science and Analytics
• Scientific Programming in Python
• Applied Statistics and Probability
• Data Management Systems
• Mathematical Methods and Modelling
• Regression Analysis
• Distributed Data Management
• Data Visualisation and Analytics
• Data Science Analytics Project

Elective modules include:

• Time Series and PCA
• Machine Learning

This structure allows students to tailor their learning experience according to their interests and career aspirations.

Careers with Data Science and Analytics

Graduates of the Higher Diploma in Science in Data Science and Analytics will find opportunities in various sectors, including technology, finance, healthcare, and government. Alumni have secured roles in data analysis, statistical modelling, and machine learning, contributing to organisations that rely heavily on data-driven decision-making.