Module: Workflow & Data Management

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Workflow & Data Management
Module Code	MT414 (ITS) / BAA1028 (Banner)
Faculty	DCU Business School	School	DCU Business School
Module Co-ordinator	Andreas Robotis
Module Teachers	-
NFQ level	8	Credit Rating	5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Coursework Only

Description

Analytics, together with innovating technologies, including big data, will be the foundation of new and better businesses. The analytical process starts with data, and future business leaders need to guarantee that the data flow for business analytics is efficient and secure. By developing skills and experience in the design and implementation of cloud-based databases and workflow, students will master some of the most used technologies for managing data analytics in organisations. To do so, students will be familiarised with some of the most current tools for database management (e.g. Apache Spark), cloud computing (e.g. AWS ecosystem), and version control (e.g. GitHub). Students will also learn about data security and how to avoid breaches of API keys and other relevant data.

Learning Outcomes

1. Have the ability to work within a Data Analytics team using cloud-based technologies.
2. Set up an efficient Data Analytics workflow while ensuring the security of the data.
3. Have the ability to explain and apply the key functions of version control applied to data analytics.
4. Critically review the existing workflow of an organisation and suggest improvements to increase its performance

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Workshop	12	No Description
Lecture	24	No Description
Online activity	24	Github Self directed learning
Assignment Completion	40	No Description
Independent Study	25	No Description
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

Data Analytics Workflow in Organisations
Analytics scrum teams and sprints; Agile analytics project; Tools for efficient team collabortion

Cloud Computing
Database and storage applications; Instances, containers, and serverless computing

Version Control
Git and GitHub/GitLab commands; Collaborative Data analytics project management

Workflow Security
API key management; Potential security breaches and weaknesses

Workflow Performance
Processing speed and power; Scalability and reliability

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Digital Project	Create and efficient and secure workflow plan workflow for a cloud based project	50%	Week 26
Written Exam	Critically review the workflow for a client, identifying any potential for performance enhancement and identifying any potential security issues.	50%	Sem 2 End

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

Caffo, B., Peng, R. D., & Leek, R. H.: 2016, Critically review the existing workflow of an organisation and suggest improvements to increase its performance, Leanpub,
Siegel, E., Glaeser, E. L., Kozyrkov, C., & Davenport, T. H.: 2020, Strategic Analytics: The Insights You Need From Harvard Business Review, Harvard Business Review,
Lisdorf, A.: 2021, Cloud Computing Basics: A Non-Technical Introduction, Apress,
Tsitoara, M.: 2019, eginning Git and GitHub: A Comprehensive Guide to Version Control, Project Management, and Teamwork for the New Developer, Apress,

Other Resources

None

This module is 5 Credits of the 20 Credits for the Business Analytics Specialism.They are all co-requisites.