Hibernia College’s new Postgraduate Diploma in Science in Business Data Analytics commences September 2022, and the application closing date is 25 August. This innovative programme will support anyone looking to progress their career in data analytics, whether it be to upskill or reskill in this exciting field. The programme’s flexibility makes it suitable for people who are working or have other commitments, and there is funding available. Read on to explore the first modules in this content-rich and industry-relevant course.
Programme Structure
The PG Dip in Science in Business Data Analytics is a one-year, 60-credit, NFQ Level 9 programme validated by Quality and Qualifications Ireland (QQI). It uses the flexible Hibernia College blended learning technology platform. The programme is founded on the three pillars of business analytics, technology and data science and will equip graduates with the essential analytics, technical, investigative and communication skills to thrive in a wide range of sectors within the modern economy.
Students will have the option to complete a project or work placement. The Analytics Institute, Ireland’s professional membership organisation for the data science and analytics industry, will coordinate work placements with its 120+ member organisations. Graduates will qualify with a Postgraduate Diploma in Science in Business Data Analytics and will also receive a professional certificate from the Analytics Institute.
Course Content
The programme begins in September with the first of three 12-week semesters. Students engage in a carefully crafted range of targeted academic and industry-focused activities, beginning with the following three modules: BDA101 Software Development for Business Data Analytics, BDA102 Understanding Data, and BDA103 Applied Probability Modelling.
BDA101 will orient students to Python programming and progress them to data preparation and exploratory data analysis. Students will be introduced to distributed environment and cloud services, including GitHub and Colab, and object-oriented programming in Python. They will gain knowledge of best practices in software engineering and conclude with a case study in Python to summarise the different steps involved in building a software tool for data analytics and using a machine learning algorithm.
BDA102 is all about data and its relevance and impact within a range of sectors. Big data, management and storage, and essential and alternative database systems will be covered. Students will develop their understanding of the Hadoop Ecosystem and data’s role in the Internet of Things (IoT).
BDA103 covers data visualisation and analysis, combinatorics and counting rules, probability theory, popular discrete and popular continuous random variables, joint distributions, Monte Carlo simulation and other advanced topics.
Flexible Delivery
Live webinars, face-to-face tutorials and laboratory tasks will be scheduled in the evenings and on Saturdays. Students will meet each other and their lecturers/tutors face to face at a venue at least once during each module. The average weekly class time totals 10 hours. There will also be additional on-demand learning where students will be required to complete additional readings, practical work and assignments. Online asynchronous sessions can be studied in students’ own time and include presentations, videos, tasks and collaborative activities. An extensive online library will be available to support students in their studies. During the programme, they will create digital artefacts, code solutions to problems, create advanced data visualisations and produce technical reports.
If this sounds like the perfect course to help you (or your colleagues) enhance a career in data analytics or business intelligence, apply today. Funded places are limited. Application and details are available on the Springboard+ website.
Feel free to email DataAnalytics@hiberniacollege.net or call our Enrolment Advisers at (01) 661 0168 (option 2) if you have any further questions about applying for the Postgraduate Diploma in Science in Business Data Analytics.