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Diploma in Data Analytics - FREE for unemployed, formerly self-employed and ‘Returners’:

-Created 08-12-2020

Big Data / Business Analysis

location Online, evenings

Course Title:   Diploma in Data Analytics - FREE for unemployed, formerly self-employed and ‘Returners’:
Course Category:   Big Data / Business Analysis
Entry Requirements
Those who are in employment/working :

For eligible applicants who are currently in employment/working 90% of the tuition fees will be covered by the HEA and the remaining 10% is payable by the student or their employer.

The Course Tuition Fee is €2,200 so €220 euro is payable by the student or their employer
Those who are unemployed, formerly self-employed and ‘Returners’:

The course is free and 100% funded for eligible applicants who are unemployed, formerly self-employed or who are classified by the HEA as ‘Returners’ or ‘Homemakers’.

Course Content
Fundamentals of Statistics for Data Analytics
The aim of this module is to provide the learner with understanding of:
1. Numerical and statistical tools used to describe and summarise data.
2. The utility and application of inferential statistical methods.
3. The purpose and limitations of regression analysis and modelling.
4. The laws of probability and their application to data analysis.
5. Software tools used for the analysis of business data

Data Exploration and Preparation
The aim of this module is to provide the learner with understanding of:
The importance of exploratory data analysis as an essential first step in the data analytical process. How to identify and handle missing and out-of-range data. Methods of encoding data for specific machine learning algorithms

Data Visualisation and Communication
The aim of this module is to provide the learner with understanding of:
1. The value of data visualisation as a means of offering rapid insights into large quantities of data.
2. The theory, concepts, techniques and processes of data representation and visualisation.
3. The types of data visualisation and their associated use cases.
4. The current range of software tools available for data visualisation

Machine Learning I
The aim of this module is to provide the learner with:
1. The role of machine learning as a tool to solve data analytics problems.
2. The purpose of data mining frameworks and their usefulness.
3. The distinction between supervised and unsupervised machine learning methods.
4. The difference between the two fundamental types of supervised learning – classification and regression.
5. Software tools used to solve classification and regression problems.

Data Analytics is among a set of emerging and rapidly developing technologies termed Innovation Accelerators, which have been identified as being critical to the next wave of digitalisation. According to Gartner’s Hype Cycle 2019, over the next decade, data analytics and AI will augment workers’ efficiency, as companies rely on leading tech to beat out competitors. Learners who complete this course will be equipped with machine learning techniques that are an essential component of data analytics. The module builds on and draws from the Fundamentals of Statistics for Data Analysis which provides learners with the ability to identify the fundamental nature of a data analytical problem. Through Data Exploration and Preparation participants obtain in-depth understanding of the rationale for data exploration and the methods used to explore data, while Data Visualisation and Communication provides the skills needed to present a variety of different types and volumes data and to display directly the results of learning achieved in previous modules.

This modular course will appeal to a range of adult learners who may or may not be currently in employment and wish to up-skill, or returners and other groups who may wish to re-skill and are seeking to develop their knowledge in data analytics on a flexible modular basis. It will give those working in IT, marketing, finance and other industries a knowledge of data analytics which will help enable them to make better business decisions.
Learning Outcomes
This course will be delivered online. Online activities can include live or pre-recorded lectures, independent learning and assessment activities such as research tasks, discussion forums, simulations, quizzes and e-portfolio work along with online group activities such as live classes, group project work, virtual labs and tutorials.

Awarding Body
Awarding Body:   QQI
Start Date
Start Date:   Week beginning 25/01/2021
Course provider information
Contact person:   CCT College Dublin
Course Email:
Contact:   Tel: +353 1 6333444
Institute:   CCT College
Location:   Online, evenings

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