Higher Diploma in Science in Data Science & Analytics

Faculty of Engineering & Science
Award Class
Awards
HDip in Sc
Programme Code CR_SDAAN_8 Mode of Delivery Full Time, Part Time, ACCS No. of Semesters 2
NFQ Level 8 Embedded Award No Programme Credits 60
Next Review Date
Review Type Date
Programmatic Review 01/09/2022
Department MATHEMATICS
 

Programme Outcomes

Upon successful completion of this programme the graduate will be able to demonstrate... :

# PO Domains Programme Learning Outcome
PO1 Knowledge - Breadth Demonstrate detailed knowledge and understanding of areas of Mathematics, Statistics, Computer Science and Business Intelligence relevant to a Data Scientist.
PO2 Knowledge - Kind Demonstrate understanding of the terminology, defining concepts and theories underlying the Data Science and Analytics field; demonstrate knowledge of the advanced methods and technologies for acquiring, interpreting and analysing big data, with a critical understanding of the appropriate contexts for their use; relate current issues in Data Science to society; understand current knowledge of the Data Science field, including current limits of theoretical and applied knowledge.
PO3 Skill - Range Demonstrate mastery of relevant skills and tools in Statistics, Mathematics, Computer Science and Business Intelligence; use these to solve complex problems involving big data sets; interpret and apply appropriate and referenced literature and other information sources; work independently within defined time and resource boundaries; communicate scientific information in a variety of forms to specialist and non-specialist audiences.
PO4 Skill - Selectivity Formulate and test hypotheses; design experiments; appreciate current limits of knowledge in the Data Science field and respond appropriately; think independently and make effective decisions; contribute fully to the day-to-day operations of the Data Science work setting.
PO5 Competence - Context Apply data analysis skills and technologies in a range of contexts in order to critically interpret existing knowledge and apply in new situations; make and report appropriate decisions in a responsible and ethical manner.
PO6 Competence - Role Act effectively under guidance in a peer relationship with qualified practitioners; participate constructively in a complex interdisciplinary team environment; plan for effective project implementation; reflect on own practices.
PO7 Competence - Learning to Learn Learn to act in variable and unfamiliar learning contexts; identify learning needs and undertake continuous learning in the Data Science field; assimilate and apply new learning.
PO8 Competence - Insight Demonstrate an understanding of the wider social, political, business and economic contexts of Data Science, including an appreciation of the philosophical and ethical issues involved.
 

Semester Schedules

Year 1 / Semester 1

Mandatory 
Code Title Module Coordinator Version Credits
STAT8006 Applied Stats & Probability David Goulding 3 5
DATA8002 Data Management Systems Ted Scully 3 5
DATA8001 Data Science and Analytics David Goulding 3 5
STAT8010 Intro to R for Data Science David Goulding 1 5
MATH8009 Maths Methods and Modelling David Goulding 4 5
COMP8060 Scientific Prog in Python Ted Scully 1 5

Year 1 / Semester 2

Mandatory 
Code Title Module Coordinator Version Credits
STAT8011 Regression Analysis David Goulding 2 5
DATA8006 Data Science Analytics Project David Goulding 4 10
DATA8008 Data Visualisation & Analytics David Goulding 1 5
DATA8005 Distributed Data Management Ted Scully 2 5
Elective 
Code Title Module Coordinator Version Credits
COMP8043 Machine Learning Ted Scully 4 5
STAT8008 Time Series & PCA David Goulding 4 5