Higher Diploma in Science in Data Science and Analytics
Level 8 About NFQ
Dr. David GouldingEmail Phone
Department of Mathematics,
Cork Institute of Technology,
Head of Department
CIT's HDip in Data Science and Analytics has been designed and developed in collaboration with industrial experts in the field of Data Science. The programme aims to furnish students with the necessary skill sets to enter the world of data science and analytics through building strong foundations in the core competencies of Statistics, Computer Science and Data Science. This higher diploma introduces students to topics including statistical modelling, regression, classification, decision trees, databases, time series forecasting and machine learning while also providing the learner with the required technical skills in software packages and industry standard programming languages including R, Python and SQL for example.
Applicants will already hold a Level 8 degree and must be highly motivated and capable of independent learning. Preference will be given to applicants with a background in cognate and analytical disciplines, who would benefit from an opportunity to rapidly and successfully convert their qualifications to industry-relevant ICT skills. All candidates with a Level 8 qualification or equivalent will be considered.
Candidates with a Level 7 qualification and significant relevant experiential learning may be eligible through our recognition of prior learning (RPL) processes. CIT has an extremely well-established and supported RPL process, please see www.cit.ie/rpl for further details.
Data Science has exploded with demand for experts across industries. The value of data to business becomes clearer each day and depends on appropriate analysis. Consequently, data science and analytics is critical for the Irish economy and is recognised as key to remaining competitive and attract and sustain investment from data centric companies such as Google, Amazon and Facebook.
The Higher Diploma in Data Science & Analytics (NFQ Level 8) is delivered jointly by the Mathematics and Computer Science departments. The programme has been designed and developed with industry experts to produce graduates that are highly skilled and competent in the core skills of programming, database management, statistical modelling, time series, machine learning, visualisation and inference. The involvement of industry in developing this course ensures it is particularly suited to address the skills shortage in the area.
The blended programme contains three themes: Data Science, Statistics and Computer Science, which are delivered across four semesters. There are significant opportunities throughout the course for learners to apply their theoretical knowledge and to develop problem solving skills through practical sessions. The learners also undertake a 10-credit capstone project with industry. This is a key opportunity to demonstrate the ability to apply learnings from the programme to an authentic problem in this field.
Graduates of this programme will be positioned to match the growing needs of the Irish and international IT industries, particularly in the Big Data space. They will be able to ally the transferable skills obtained in their previous undergraduate degree to newly acquired knowledge, skills and competences from this program, including solving real-life problems. Potential job opportunities not only include those of data scientist, data analyst or data engineer, but also skilled staff who gain actionable insight from data to enable better decision making.
During the programme, students will undertake the following modules (the module DATA8006 is a 10 credit module while the others are 5 credits):
DATA8001 - Data Science and Analytics
This overarching module will provide the learner with an overview of the important themes in the growing field of data science and analytics. The learner will study the established methods and technologies and also investigate new and emerging trends. Emphasis will be placed on statistical theory, mathematical algorithmic design and modelling concepts. The context and use of data analytics in real world setting will be investigated with topics such as data privacy, data security, and ethics.
COMP8043 - Machine Learning
The module will provide a comprehensive foundation in the application and implementation of machine learning techniques. The module will focus on supervised and unsupervised learning algorithms, specifically classification, regression and clustering techniques. It will also look at the theory of optimization and examine its application to high dimensional search spaces.
DATA8002- Data Management Systems
This module introduces students to the use of database management systems for applications. It includes an evaluation of the relational model and NoSQL data models, and how to query and manipulate data stored using these models. Students will learn how these data models are used in the distribution of data and the emerging "Big Data" paradigm.
STAT8006 - Applied Statistics and Probability
This module will apply statistics and probability distributions to modern day problems. It will develop graphical visualisation methods, probability theory and distributions. The module will develop knowledge, skill and competence of sampling theory and hypothesis testing using both parametric and non parametric methods.
COMP8060 - Scientific Programming in Python
In this module, the learner will use the Python programming language to manipulate, manage and process data. More specifically, statistical and numerical libraries will be applied to analyse and manipulate complex data sets. The learner will also use Linux commands to perform basic system and file operations.
STAT8010 - Introduction to R for Data Science
In this module, students will learn how to clean, manipulate and visualise data using the statistical software package R. Students will create and analyse statistical models and simulations with R.
DATA8005 - Distributed Data Management
In this module the learners will be survey the main NoSQL-based data models as an alternative to the traditional relational model. The learner will also explore the ecosystem of a big data framework, with an special focus on the data storage and the application of large-scale data analysis libraries.
DATA8008 - Data Visualisation and Analytics
Data visualisation is of growing interest in the field of data science and analytics. In this module, the learner will investigate a variety of advanced visualisation concepts and tools for analysing multi-dimensional data, large data sets and geospatial data. The learner will also examine major statistical modelling trends and challenges within the field of data science and analytics
STAT8011 - Regression Analysis
Regression analysis is the most widely used tool in statistical modelling. Students will examine regression in the context of industry based data linked to experimental design. Students will also learn how to conduct ANOVA and logistic regression which are widely used in industry.
STAT8008 - Time Series & PCA
This module introduces learners to the concepts of data dimension reduction and principle component analysis. Furthermore, it provides the learner with the necessary tools to develop and critically evaluate time series models. The forecasting function of time series models is presented and evaluated, enabling the learner to create short and medium term forecasting models.
MATH8009 -Maths Methods and Modelling
This module will explore various mathematical techniques and will focus on mathematical models of real world processes, their formulation and methods of solution - both numerical and analytical. Central to the module will be practical problems that arise in industry and commerce.
DATA8006 - Data Science & Analytics Project
This module develops within the learner the knowledge, skills, and competences required to research, develop and scope a data science/analytics project, and to successfully complete it in accordance with an approved plan. The module requires the learner either individually or as part of a team to develop, implement and critically assess a detailed methodology to address a defined data science or analytics problem within a prescribed time-frame. The learner is expected to be self motivated whilst working under direction of a project supervisor and to communicate the process and outcomes of their work in a style and manner appropriate for professional practitioners in the discipline.
This is a part-time blended delivery programme run over four semesters. In each of the first three semesters, learners will undertake three taught modules while in the final semester the learner completes one taught module and a research project.
Apply online through www.springboardcourses.ie
Please upload a current Curriculum Vitae, a Personal Statement and scanned copies of the documents listed below:
1. Personal Statement - Please include a 300 word personal statement detailing what has motivated you to undertake the programme and how the programme fits in with your career objectives.
2. Educational Transcripts for any qualifications listed in your application form.
3. Final Certificates for any qualifications listed in your application form.
4. Please also include any further information which you think would support your application, for example, information in regard to Higher Level grades in Leaving Certificate Mathematics, Leaving Certificate Applied Mathematics, Leaving Certificate Physics. (You must provide evidence by way of final certificates).