Higher Diploma in Science in Data Analytics
Course Overview
This one year full-time course will equip participants with the theoretical and practical skills to enter the world of data analytics. It will provide learners with the required skills in the areas of statistics, programming and databases that will enable them to transfer much of the domain specific knowledge/skill already gain in their primary degree(s) to the IT and Data Analytics sector.
This course is aimed at candidates with strong numeracy skills and a basic knowledge of statistics who wish to become more data savvy, no prior programming exposure is required.
Career Opportunities
The course provides the necessary preparation for a career in areas such as
- Data Analyst;
- Data Scientist;
- Data Engineer;
- Business Analytics Specialist;
- Data Visualization Developer;
- Analytics Manager
Course Delivery and Modules
This course will run over 1 year, with online delivery over 3 days per week. Delivery will consist of a combination of online classes running Monday to Wednesday (daytime and evenings) and will involve a blend of "live" synchronous classes supported by additional asynchronous contact hours (videos/practical tasks), to be completed within the same week but at a time of the student’s choosing.
Weekly classes will be interactive, with hands-on practical lab-based sessions supported by independent and online learning. Participants are required to attend online synchronous classes as this will be necessary in order for them to master the materials.
Any End-of-semester final exams will take place onsite in DkIT, and participants will be required to attend.
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Modules
- Statistics using R (10 credits): This is a year-long module which lays solid foundations in statistics, through R.
- Spreadsheet Data Analytics (5 credits): This module focuses on the use of Excel for once off analysis of existing data sets.
- Applied Database Systems (5 credits): Focuses on databases with large amounts of flat data as they appear in Big Data and Machine Learning
- Ethics and Social Responsibility in Data Analytics: Provides students with an understanding of the moral considerations that are foundational in Data.
- Advanced Statistics using R (10 credits): Focuses on non-parametric modelling, generalised linear models, and machine learning.
- Real Time Data Analytics (5 credits): Applying data analytics techniques on real-time data or data streaming with minimal latency.
- Research Methods for Data Analytics (5 credits): Introduces students to good research practice and prepares them to conduct their industry project.
- Data Visualisation (5 credits): Focuses on the effective use of visualisations to both understand data and communicate findings.
- Data Analytics Project (10 credits): Students will undertake, with appropriate supervision, an industry related data-driven project.
Education Progression
Fees and Funding
EU Fees:
- Full-time (EU): €3,819
- Part-time (EU): n/a
Non-EU Fees:
- Full-time (EU): €9,950
- Part-time (EU): n/a
You may be eligible for financial support for this course through the Student Universal Support Ireland (SUSI) system. Find out more
Fees
€3,819
€9,950
Entry Requirements
- ANY Honours (Level 8) Degree with 15 credits of Mathematics
and/or
- Statistics OR Level 7 Degree with 15 credits of Mathematics and/or Statistics AND at least 2 years of work experience together with strong numeracy skills.
No prior programming exposure is required
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Disclaimer: All module titles are subject to change and for indicative purposes only. All courses are delivered subject to demand and timetables are subject to change. Elective Module options will only run subject to student numbers. The relevant Department will determine the viability of each elective module option proceeding depending on the number of students who choose that option. Students will be offered alternative elective modules on their programme should their preferred elective option not be proceeding. Award Options for Common Entry Programmes: The relevant Department will determine the viability of each award option proceeding depending on the number of students who choose either option. If the numbers for one of the Award options exceed available places, students for this option will be selected based on Academic Merit (highest grades).
