Postgraduate Certificate in Computing (Data Science) Online

Course Summary

The part-time online Postgraduate Certificate in Data Science offered by ATU Sligo prepares graduates for future work opportunities in data analytics, big data and artificial intelligence. Data Science is an occupation in high demand with strong employment growth across multiple industry sectors. Our unique embedded Certificate programme comprises 7 taught modules , providing foundational skills in data science.

Data Science is a multi-disciplinary field that attempts to extract knowledge from structured and unstructured data. It combines techniques from mathematics, statistics, information theory, computer science and artificial intelligence with applications in a wide variety of fields.

The programme is delivered part-time online .

Semester 1 | 2 Modules | Introductory Programming for Data Science and Applied Statistics and Probability
Semester 2 | 3 Modules | Data Analytics and Visualisation, Applied Linear Algebra, Machine Learning
Semester 3 | 2 Modules | - Programming for Big Data and Research Methods 

The  programme includes modules in the following areas:

Introductory Programming for Data Science
Programming for Data Science will introduce the learner to the core concepts of data science programming. The student will be introduced to the Python programming language (specifically SciPy) generally, and will employ functions to manipulate lists, before implementing multi-dimensional arrays using Numpy or similar in order to perform statistical operations and linear equations. The student will then manipulate data frames and time-series data using pandas or similar. SQL programming will also be introduced. Finally, the student will create, populate and query a NoSQL cloud database.

Applied Statistics and Probability
This module covers the statistics and probability required for a MSc in Data Science. The learner will gain the expertise to interpret the probabilistic models used in the appropriate literature. It will cover statistical methods to analyse and quantify processes. It will enable learners to model problems using probabilistic and statistical mathematical methods.

Data Analytics and Visualisation
This module covers the data analysis and visualization skills required for a Postgraduate Certificate in Data Science. This topic will introduce the learner to data analysis techniques, which helps to interpret and extract meaningful information from raw data. The learner will gain the expertise in data pre-processing, exploratory data analysis and visualization, pattern recognition and discriminative classification. The learner will work on solving real-world problems.

Applied Linear Algebra
The subject covers the linear algebra required for post-graduate engineering and computing courses. The learner will gain the expertise to interpret the linear algebra models used in the appropriate literature. It will also enable learners to model problems using linear algebra methods.

Machine Learning
This module introduces the topic of machine learning algorithms (algorithms that learn from data), with the first part of the module dedicated to the standard shallow forms of machine learning before moving on to Deep Learning and Convolutional Neural Networks for use in computer vision tasks, particularly recognition, classification and localisation. The emerging topic of Deep Reinforcement Learning will be briefly introduced. The module will look at training strategies and frameworks for Deep Learning. As well as the technical/scientific elements, students will reflect on the ethical implications of machine learning.

Programming for Big Data
This module introduces students to the architectures and tools underpinning the management and processing of large scale datasets, which are too big for conventional approaches. Students will understand these architectures and tools and be able to use them to code solutions, query data from structured, unstructured, and streamed sources, and analyse that data using appropriate algorithms.
Students will also be able to evaluate a variety of Big Data Cloud platform providers e.g. Amazon AWS, Microsoft Azure, in order to deploy and host data solutions.

Research Methods

To ensure the integrity and accuracy of data science research, it is essential that the student develops a deeper understanding of the fundamentals of research methods and ethics.  This module introduces skills needed to develop meaningful research questions within particular problem domains, and to design appropriate research strategies and designs to address these.

For further information please contact: 

T: 353 (0) 71 931 8510
E: admissions.sligo@atu.ie

Key Course Information

The Postgraduate Certificate in Computing (Data Science) is fully online, delivered over 1.5 years, consisting of seven taught modules . 

Study hours: Whether you are studying part-time online, blended or full-time online, it is very important that you allocate enough study time to your online course to stay focused, reduce stress and achieve your goals. For part-time online or blended learning, it is recommended that you should try to allow for 5-6 hours per week per 5 credit module to your studies.

Live Lectures: Live lectures normally take place between 6pm and 10pm, Monday to Thursday but this may vary depending on the availability of specific lecturers. For the MSc in Computing (Data Science), students are expected to attend 2-3 lectures each week which can range from 1-3 hours. If the Live Classroom scheduled times for the live online lectures do not suit you, recordings will be made available through Moodle.

This online programme has a January Intake only each year.

Application Closing Date : 7th January 2025

Entry Requirements

To qualify for entry to the programme a standard applicant must hold a Level 8 Honours Degree 2:2 or above. While it expected that most applicants will have an honours degree in Computer Science or related discipline, due to the multidisciplinary nature of Data Science an Honours Degree in Engineering, Business and Science will be sufficient. However, some programming knowledge (Ideally Python or C/C++ or R) and mathematics knowledge are prerequisites to the course.

Graduates who have not obtained this minimum may incorporate other equivalent qualifications and relevant work experience and apply for assessment via the Recognition of Prior Learning (RPL) process. RPL is a process that may allow you to gain admission to a programme or to receive exemptions/ credit for some parts of the programme based on demonstrated learning that you may have achieved through another programme of study or through your work or career. Further information is available at www.atu.ie/recognition-of-prior-learning which our dedicated RPL portal or by contacting our admissions team at admissions.sligo@atu.ie .

Career Opportunities

Data Scientists will implement and assess the models for increasingly complex and technical roles currently required by industry. Skills such as Artificial Intelligence and Data Science are highly sought after in manufacturing, insurance, retail, health & education sectors to harness opportunities associated with researching and making decisions based on data. 

A qualification in Data Science skills can lead to an exciting career in an array of industries including IT, financial services, retail, and manufacturing. These roles are not confined to IT based positions but can lead to roles in business intelligence, analysts, or data warehouse consultants. 

Further Study

Students completing the certificate can at a later stage undertake a 3 semester thesis, leading to the award of an MSc in Computing (Data Science).

Testimonials

Ross McMorrow is a graduate of the Masters in Data Science, as a part-time online student at ATU Sligo. Ross previously studied Mechanical Engineering and Mechatronics and has worked in industry for several years in Maintenance and Technician roles.

Coming from an Engineering background, Ross believes that the Masters in Data Science is opening up a world of opportunity for him, particularly with the Industry 4.0 movement. The lines between Engineering and IT are blurring, with new opportunities for Engineers with data science skills evident across the Manufacturing sector.

As part of the course, Ross has mastered skills in Python programming, giving him the ability to extract and manipulate machine data. He can see the practical application of the skills already. Extracting data from multiple sources and using Machine Learning algorithms to predict optimum machine maintenance schedules. Ultimately these skills will help minimize machine downtime and can result in real commercial benefits.  

With Industry investing in upgrading and modifying older production lines to transition into smart facilities, Ross can see how modules such as Machine Learning, Data Visualization and Statistics will be relevant to the Engineers of the future.  Opportunities to build Big Data sets by merging external sources, machine and historical data open huge opportunities for machine learning algorithms to streamline data management within industry.

With companies rapidly ramping up efforts to maximize their digital business model, the current skills shortage of Data Scientists makes the career choice attractive to Engineers from diverse backgrounds, attracting high salaries across industry.

Programme Fees

Academic Year 2024/25 Fees

Total Programme Fee: €3,000

To help make the payment of fees more manageable for students who are self-funding their studies, tuition fees can be paid through payment instalment plans at ATU Sligo. For further information on instalment plans, please visit our Fees and Funding webpage. 

For further information and guidance about Fees and Funding for online and part-time courses at ATU Sligo, click here.

If you are seeking to take your exams online, and you meet the eligibility criteria (overseas students and those with extenuating circumstances), an additional examinations fee will apply. For further information, please visit our Examinations webpage. 
 

Course Format

Semester 1

Semester 2

Semester 3