Module: Data Analytics & Story Telling

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Data Analytics & Story Telling
Module Code	MT5000 (ITS) / BAA1030 (Banner)
Faculty	DCU Business School	School	DCU Business School
Module Co-ordinator	Damien Dupré
Module Teachers	Gerard Conyngham
NFQ level	9	Credit Rating	5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Coursework Only

Description

Data and visual analytics is an emerging field concerned with analyzing, modeling, and visualizing complex high-dimensional data. This course will introduce students to the field by covering state-of-the-art modeling, analysis, and visualization techniques. It will emphasize practical challenges involving complex real-world data and include several case studies and hands-on work with programming languages (SQL and R) and visualisation software (Tableau and PowerBI).

Learning Outcomes

1. Explain the term Data Analytics and summarise the challenges and opportunities for businesses of effectively mining, managing, analysing and reporting on their data.
2. Compare and contrast traditional relational database management systems with non-relational databases developed for dealing with 'Big Data'
3. Apply suitable data visualisation techniques do a wide variety of variable types and develop dashboards using Tableau.
4. Use R to create professional data analytics reports on real-case scenarios applied to the world of business and management
5. Detail the most recent techniques and applications for analysing databases in organisation environments.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	22	The lecturer will present the essential ideas and core concepts pointing students towards resources where they can get further information
Independent Study	65	Preparation for, and reading after lectures
Assignment Completion	38	Assignments listed in Coursework above
Total Workload: 125

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Introduction to Data Analytics
What is Data Analytics? Data Sources and types. Data Analytics strategies for managers; managing data, mining data, analysing data, and reporting on findings. Tools for managing, storing, and analysing data for both traditional and non-traditional datatypes.

Data Visualisation
Tables and Graphs, Functions of Visualisations, Graphic Integrity, Data-Ink Ratio, Tables & Graphs, Multiple Datasets, Interactive Graphs. Use of Tableau to develop Data Visualisation Dashboards.

Descriptive Statistics and Statistical Modelling
Summary of advanced techniques for analysing quantitative data

Database / Data Management
SQL Databases, Relational Management Database systems (RMDS) Big Data storage and management

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Assignment	Develop a Data Visualisation Dashboard using Tableau	50%	Week 25
Assignment	Create a Data Analytics report using R	50%	Week 28

Reassessment Requirement Type
Resit arrangements are explained by the following categories: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
This module is category 1

Indicative Reading List

Claus Wilke: 2018, Fundamentals of Data Visualization, https://clauswilke.com/dataviz/,
Danielle Navarro: 2016, Learning statistics with R: A tutorial for psychology students and other beginners, https://learningstatisticswithr.com,

Other Resources

None