Postgraduate Certificate in Data Analytics with Python
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Course Overview
This programme builds advanced Data Analytics skills that can be applied across a variety of sectors. Graduates will be equipped with the knowledge and skills necessary to tackle data-driven problems, leveraging data to gain insights and drive informed decision-making.
The primary aim of this 20-credit level 9 postgraduate programme is to develop advanced knowledge in aspects of data required to become skilled Data Analysts: Statistics and Statistical Programming, with a focus on applied industry applications. Learners will build knowledge in Statistics and Statistical Programming which can be applied across a variety of sectors. This knowledge can be combined with the domain-specific knowledge/skill already gain in their primary degree(s) to improve graduates’ employment mobility and/or to leverage advancements in Data Science within their current employment setting.
Students will build advanced digital competencies, develop critical thinking and specialist analytical skills together with business acumen, in an area highlighted as crucial for the workplace of the future. The programme is underpinned by industry driven problems taken directly from domains in which learners are experts. The applied focus is achieved within modules, through case studies and data-driven projects together with a cross-modular Data Project, between modules: Statistics and Programming for Data Analytics.
Students will work through various stages of the Data Analytics Lifecycle – from framing a question, through identifying, managing and storing appropriate data, to analysing, modelling and interpreting outputs and finally to visualising and communicating findings for a diverse audience. Thus, allowing the leverage of data to gain insights that can inform decision-making
What makes this course different
Exciting New Skills through funded programme
Equip yourself with key skills necessary for decision-making and competitiveness in a data-driven world, through this funded, flexible industry focussed programme.
In-Demand Graduates
A skills shortage in data analytics has been highlighted by the Department of Enterprise, Trade and Employment, with big data analytics listed as a critical skill.
Understanding the Industry
Information and Communications Technology (ICT), in particular data-driven technology, is a constant and integral element of the world we inhabit and affect. Advances in technologies such as Artificial Intelligence, Robotics and the Internet of Things are transforming the way we live and work. They are radically affecting how business is done and the Fourth Industrial Revolution is set to dramatically change society.
The disruptive impact of such change on world labour markets has already been felt and is anticipated to grow in an increasingly data-driven future. Skilled ICT workers are the driver of new development and of economic growth, and access to highly-skilled, analytical and adaptive citizens capable of dealing with, and making sense of, the massive amount of available data, is becoming a key factor in the success of companies worldwide.
The past decade has seen increased recognition of the importance of numeracy and mathematical skills in the technology sector, and acknowledgement that the ability to generate, understand and analyse empirical data is crucial for technological advancement. We note that the OECD Skills Outlook 2017, in discussing the types of skills that give countries global advantage, pointed to the increasing need for numeracy and mathematical skills, particularly in technologically advanced industries.
Today, data is viewed as the new international currency and the data-driven global economy is regulated by this incredible but revolutionary change from data poor to data rich. This can be seen in the domination by numerous data-centric capital entities. In-depth research of the storage, analysis and use of big data is attracting interest, from giant private data-centric enterprises to smaller data-driven companies, major government organisations and academic institutions. Examples include data-centric projects in Google, Facebook, Amazon, and Alibaba, the global pulse project initiation by the United Nations and strong EU data legislation, GDPR, to regulate the use and analysis of personal data.
We are living in the age of big data, data analytics and data science. The growth of big data presents tremendous knowledge, insights and challenges that result in ingenious innovations and economic growth opportunities. The availability of the right data and the ethical considerations around its use clearly comes into play.
Data-driven technology is changing the way we communicate, learn, live, work, and entertain. Hence, it is essential that we as a society develop the capability to utilise and understand data, it benefits and its implications. This is a view that is reflected in the Strategic direction and goals set out by many government agencies.
Career Opportunities
Graduates will be positioned to take on different roles, including:
- Data Analyst
- Data Scientist
- Business Analytics Specialist
These career paths can take place in many different sectors and industries including:
- research
- academia
- business
- finance
- banking
- healthcare
- pharmaceuticals
- commercialism
- technology
Course Delivery and Modules
This online course will be delivered over 1 semester, and will commence with a hybrid “course induction and boot-camp”. Weekly classes will be delivered in a live synchronous format, 2 evenings per week, typically Mondays and Wednesdays from 6pm to 9pm.
Attendance and participation in live classes will be important in order to master the material. Live online classes will be support by additional asynchronous activity (videos/ resources and tasks) which participants complete in their own time within the same week. There will be 1-2 additional days of live class per semester, which will be facilitated both onsite and online.
Participants will be required to attend onsite for any formal end-of-semester examinations, these will occur during examination periods in Jan/ May, but students will be advised of dates well in advance.
This course contains just one such examination, Statistics, which occurs in semester 1.
The statistics module aims to build students' knowledge in statistics and probability and apply these techniques to varied datasets. This module works in harmony with the Programming for Data Analytics module to ensure the students can apply these analytical and modelling techniques to real data. Knowledge built and applied will be showcased in a cross-modular project at the end of the first semester.
The Programming for Data Analytics module will teach students about data structures and programming techniques which will allow them to gather, manipulate, store and graph data sets from a variety of case studies. The programming language used will be python, which provides numerous of libraries relevance to Data Analytics. This module runs in parallel with Statistics and culminates in a cross-modular Data project.
Education Progression
Depending on their previous educational path, graduates from this programme may choose to progress to a Masters programme in the area of Data Analytics.
MSc in Data Analytics
Fees and Funding
This is a PEACEPLUS-funded programme, delivered via the SECBA project. More details on fees to follow.
Entry requirements
Standard Entry Requirements
The standard minimum entry requirement is a 2.2 Honours Degree (NFQ Level 8) or equivalent in STEM (Science, Technology, Engineering, and Mathematics), or equivalent qualification in a cognate discipline, which provides a strong foundation in Mathematics/Statistics and computer applications.
Graduates from non-STEM disciplines who have developed required numeracy and technology skills as part of their studies (for example, graduates with a 2.2 from level 8 financial programmes, such as accountancy, economics, etc. or psychology programmes which incorporate quantitative and technology-related modules) will also be eligible for consideration.
Competency in Computing (Databases, Programming and/or Data Analytics tools/ platforms) is a requirement for this course. This may be achieved formally (as part of their programme of studies), informally (professional non-certified courses) or non-formally (in the workplace).
Where required, interviews will be used to assess applicants’ numeracy and familiarity with Computing Applications.
Recognition of Prior Learning
Applicants who do not meet the standard academic entry requirements but have significant relevant experience (certified and/or experiential) may apply to access this programme via the Recognition of Prior Learning (RPL) route. Learn more about RPL at DkIT
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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).