Higher Diploma in Science in Data Analytics (Full-Time)
Four great reasons to consider this course
- This course is ideal for numerate graduates looking to enhance their skills and improve their employability in the field of Data Analytics.
- It is delivered by highly qualified lecturers with significant professional experience.
- Underpinned by concrete theories, this course provides real practical statistics and computing skills that can be used to solve business problems across a wide range of industries.
- This course is delivered through a blended combination of online and onsite learning.
This one year full-time course (funded via Springboard+) 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. This one-year full-time course is funded via Springboard+ and is created for those who wish to develop the skills, expertise and knowledge to work as Data Analysts and is aligned with the needs of local data focused companies.
Modules on this programme include:
- 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.
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.
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
ANY Honours (Level 8) Degree with 15 credits of Mathematics
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.
Graduates with strong numerate skills who wish to pursue a career in the expanding area of Data Analytics.
Professionals who wish to develop their Data Analytics and Mining skills to apply them to real problems in their current work domains.
Graduates who enjoy Mathematics and problem-solving with a focus on real-world problems.
Graduates who are interested in developing their advanced statistical and computing skillsets to master areas such as statistical inference and machine learning.
|School||School of Informatics & Creative Arts|
|Department||Computing Science and Mathematics|
|Awarding Body||Dundalk Institute of Technology|