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MIS41430

Academic Year 2024/2025

Mastering Big Data with AI Enabled Citizen Development (MIS41430)

Subject:
Management Information Systems
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
7.5
Module Coordinator:
Dr Martin Perry
Trimester:
Summer
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

One of the main obstacles to digitally transforming their business and managing the growth of data generated is the lack of professional IT staff, according to 86% of IT decision-makers. Citizen Developers are essential for organizations in filling this resource gap. But who are the Citizen Developers? And what do they do within an organization? And how can they help? The global market for Low Code Development Platforms, which was US$12.8 Billion in 2020, is expected to reach US$125.8 Billion by 2027, with a Compound Annual Growth Rate (CAGR) of 38.6% from 2020 to 2027. This course will cover no code/low code technologies to collect, clean, analyze and share big data insights for the enterprise. Both commercial and open-source software solutions will be discussed.

About this Module

Learning Outcomes:

• understand digital transformation and generation of big data
• benefits to master this Big Data
• no-code/low-code platforms available.
• best practices to securely turn ideas into applications and harness the data
• benefits to organisations in terms of speed and cost
• governance and controls to be developed to oversee initiatives and
mitigate risk
• unlocking the full potential of citizen development talent within teams
• What does success look like? Identifying Success Factors.

Indicative Module Content:

• What is Citizen Development, and why does it matter?
• Definition of Big Data in Enterprise
• The Data Driven Enterprise
• My data is a mess! How to tame and transform into tidy data
• Build automated workflows between collection, cleaning, analysis of data, and sharing of insights
• Low-code custom apps for business needs.
• Share insights with reports & dashboards in a cloud & mobile focused environment
• New ways of interacting with Big Data - conversational virtual assistance.

Student Effort Hours:
Student Effort Type Hours
Lectures

30
Specified Learning Activities

55
Autonomous Student Learning

80
Total

165

Approaches to Teaching and Learning:
This module is based on a mixture of lectures, laboratories and assigned homework and group work.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Participation in Learning Activities: There is a range of in-class and homework assessment assigned throughout this module. A final composite grade is awarded based on three individual project components. Week 10 Graded No
100
 Yes

Carry forward of passed components
No
 

Resit In Terminal Exam
Autumn No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Dr Martin Perry Lecturer / Co-Lecturer
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