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Master of Science in Data Science & Analytics

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Awards
MSc
Programme Code: CR_SDAAN_9
 
Mode of Delivery:Full Time, Part Time, ACCS
 
No. of Semesters:3
NFQ Level:9
Embedded Award:No
 
Programme Credits:90
programmeReviewDate:September 2022
Department:MATHEMATICS
Field of Study:Mathematics
 

Programme Outcomes

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

PO1 Knowledge - Breadth
  (a) Demonstrate comprehensive and systematic understanding of the facts, principles, theories and methods from the fields of Mathematics, Statistics, Computer Science and Business Intelligence which are relevant to the Data Analyst.
  (b) Identify and articulate the key considerations of a “Big Data” problem; draw complex information together; critically comment on the technical, social, economic, environmental and political implications of own work and the work of others in Data Science, including an appreciation of the philosophical and ethical issues involved.
PO2 Knowledge - Kind
  (a) Demonstrate comprehensive knowledge and detailed understanding of: the theories, paradigms, defining concepts and underlying principles of the rapidly evolving Data Science and Analytics field; demonstrate knowledge and experience of advanced and new methods and technologies for acquiring, interpreting and analysing big data, with a critical awareness of the appropriate contexts for their use through the study of original papers, reports, journals, and data sets; demonstrate comprehensive knowledge and understanding of: the identification, definition and resolution of novel, complex research problems; relevant legal and regulatory frameworks; aspects of the defining elements and the inter-relationships of Data Science & Analytics as a result of in-depth study and research; demonstrate comprehensive knowledge and appreciation of the current limits of theoretical and applied knowledge in interdisciplinary field of Data Science and Analytics.
PO3 Skill - Range
  (a) Demonstrate mastery of standard and specialised research tools in Statistics, Mathematics, Computer Science and Business Intelligence; use these to proactively model, troubleshoot and solve original technical problems in the “Big Data” space; source relevant information, critically interpret and apply appropriate referenced literature from a wide range of information sources; maintain detailed records of activities; present and defend scientific research findings in a variety of forms to data scientists, “data savvy” practitioners, and non-specialists; formulate a hypothesis and design a relevant programme of investigation; work independently within defined time and resource boundaries; write accurately and in a manner consistent with scientific publications in Data Science or related disciplines.
PO4 Skill - Selectivity
  (a) Design, develop and test novel hypotheses; design experiments; select from a range of scientific skills, in particular those which draw from Mathematics/Statistics and Computer Science, so as to apply the most appropriate in a range of situations; think independently and make informed effective decisions; make decisions in the Data Science work setting; develop new skills either independently or with minimal mentoring.
PO5 Competence - Context
  (a) Apply advanced research skills and Big Data technologies; act autonomously and think independently; constructively criticise, draw conclusions and offer recommendations in a wide range of contexts, including unpredictable situations; formulate and communicate judgements, with incomplete or limited information.
PO6 Competence - Role
  (a) Act effectively, demonstrate initiative, lead and take responsibility in a complex interdisciplinary team environment with data scientists and qualified practitioners of other disciplines; develop and implement novel technical solutions for “Big Data” problems; reflect on own practices.
PO7 Competence - Learning to Learn
  (a) Learn to act in variable and unfamiliar learning contexts; identify knowledge gaps through effective self-evaluation; source and undertake self-learning as necessary for continued academic and professional self-development as a Data Scientist.
PO8 Competence - Insight
  (a) Identify and articulate the key considerations of a “Big Data” problem; draw complex information together; critically comment on the technical, social, economic, environmental and political implications of own work and the work of others in Data Science, including an appreciation of the philosophical and ethical issues involved.
 

Semester Schedules

Stage 1 / Semester 1

Mandatory
Module Code Module Title
DATA8002 Data Management Systems
STAT8010 Intro to R for Data Science
MATH8009 Maths Methods and Modelling
COMP8060 Scientific Prog in Python
DATA8001 Data Science and Analytics
STAT8006 Applied Stats & Probability

Stage 1 / Semester 2

Mandatory
Module Code Module Title
COMP9060 Applied Machine Learning
STAT9004 Statistical Data Analysis
DATA9002 Distributed Data Management
MATH9001 Research Methods
DATA9005 Data Analytics & Visualisation
STAT9005 Time Series & Factor Analysis

Stage 1 / Semester 3

Mandatory
Module Code Module Title
DATA9003 Research Project -Data Science

Cork Institute of Technology
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