Data Literacy & Analytics for the 21st Century

This micro-credential equips students with the knowledge and skillset of Data Literacy and Analytics required in the 21st century. It enables students to gain the capability to collect, process, critique, analyse, visualise, and interpret data in an unbiased, responsible, actionable and ethical manner. It further prepares students with the ability to use tools and techniques to efficiently understand, interpret, and use data in their discipline. 

The delivery mode will be online and asynchronous. There will be no synchronous face-to-face or online lecture/lab taking place throughout the semester. All resources will be available to students on the module Loop page and students will complete the topics at their convenient time in a self-paced manner. For attaining the best out of this module, students are highly advised to engage with the content every week over the semester. 

There are seven online assessments corresponding to the seven topics. Students are encouraged to take the assessment immediately after they complete the content covered in that topic. Students are expected to pass all the seven topics to pass this module. The passing mark for each topic will be posted on the module loop page. Students are also allowed to try as many times until they pass the assessment during the term. However, failing to achieve the passing mark for three consecutive attempts may require them to revisit the content covered before trying again.

Upon successful completion of this micro-credential students will be able to:

1. Describe the fundamental concepts of Data Literacy and Analytics, the key steps in the analytics process, and the applications and implications of data analytics in their specialism.
2. Demonstrate knowledge of big data analytics and its steps, key statistical concepts underlying data analytics techniques, including descriptive statistics.
3. Discuss the ethical and legal requirements involved when gathering, storing, analysing and reporting data and the societal impact of data analytics.
4. Be able to source and import data and apply basic operations for cleaning, and processing this data in preparation for data analysis using basic functionalities of analytics tools such as Spreadsheet, Python, or R.
5. Identify and use basic data analysis and visualisation tools to describe and interpret data.
6. Identify the relevant insights extracted from a dataset and effectively and appropriately communicate (interpreter, critique, and present) them.
7. Differentiate between the different data types in analytics and have the ability to explain basic database design concepts, including conceptual, logical, and physical data models.

A Primary Honours degree, Level 8 in Electronic/Electrical/Computer Engineering, Applied Physics, Computer Sciences or other Cognate/Engineering Disciplines. Applications are also invited from diverse educational and/or employment backgrounds, with applications evaluated on a case-by-case basis. 

And also to indicate the required documentation:

• Please provide Academic Transcripts for final year of study where appropriate (English translation)
• All applicants must submit a copy of their passport

There is no availability for a deferred entry onto a micro-credential.

If applicable, evidence of competence in the English language as per DCU entry requirements.  Please see here.

For further information regarding the HCI learner subsidy eligibility criteria please click here. (https://hea.ie/skills-engagement/hci-pillar-3-micro-credentials-learner-fee-subsidy/).