Course Description

This subject of Technology, Data & Analytics aims to enable students to understand the challenge of managing large data sets and to provide them with a skill set to meet this challenge. It combines academic rigour and practical relevance. There is a careful balance of teaching and learning, individual and group work. Case study methodology and class-based discussions are used to strengthen the conceptual, analytical and problem solving skills of the participants in real situations.

Course Objectives

•     Students will discover the fundamental methods and frameworks to help them make sense of their data and use it to make informed business decisions.
• Students can understand the challenge of managing large data sets and provide them with a skill set to meet this challenge.
• Students can demonstrate the analytical skills to interrogate large data sets in a business environment; 
• Students can demonstrate a critical awareness of current issues in Business Analytics
• Students can demonstrate that they have obtained a set of personal development and lifelong learning skills applicable to the international business environment.

Course Outline

Week 1 – Maths & Statistics Primer

•     Introduction to probability theory
•     Basics of probability & statistics
•     Probability models
•     Bayes’ rule and conditional probability
•     Total probability
•     Bayes’ rule application

Week 2 – Maths & Statistics Primer

•     Probability distribution
•     Binomial distribution
•     Central limit theorem
•     Manipulating normal variables

Week 3 – Python Primer

•     Operating systems overview
•     Variables in python

Week 4 – Python Primer

•     Creating and managing lists
•     Numerical lists
•     Tuples
•     Dictionaries in python

Week 5 – Python Primer

•     Boolean variables
•     Conditional variables
•     About functions
•     Python demonstration and code manipulation

Week 6 – Descriptive Analytics 

•     What is data?
•     Data and decision making

Week 7 – Descriptive Analytics

•     Estimate statistics of a data set
•     Maximum likelihood estimation
•     Detection and quantification of correlation

Week 8 – Descriptive Analytics

•     Outliers
•     Linear regression
•     Real-life applications

Week 9 – Predictive Analytics (4 weeks)

•     Introduction to machine learning
• Machine learning process
• Supervised learning

Week 10 – Predictive Analytics

• Forecasting vs inference
• Using nearest neighbours for classification problems
• Predict outcomes in a business context using regression trees
• Classify data using support vector machines

Week 11 – Predictive Analytics

• Measure similarity of data clusters
• Predict outcomes for different clusters
• Machine learning in the real world

Week 12 – Prescriptive Analytics

•     Foundations of linear programming
•     Optimisation problems
•     Production planning problem
•     Capital budgeting problem

Week 13 – Prescriptive Analytics

•     Identifying the constraints
•     The optimal solution
•     Solving the problem in Excel
•     Model business problems as linear programmes

Week 14 – Prescriptive Analytics

•     Integer programming
•     Optimisation models
•     Tricks-of-the-trade for business decisions
•     Real-life applications

Text books

• Liba. (2020). Guide to Business Data Analytics. International Institute of Business Analysis
• ●  Turban, Efraim., Delen, Dursun., & Sharda, Ramesh. (2021).Business Intelligence, Analytics, and Data Science: A Managerial Perspective. Kindle Edition. Pearson

Course Description

This subject goes beyond high-level foundational theory and the hypothetical to provide a fully immersive deep-dive into the skills, methods and tools employed by service designers everyday, whilst adding in some exclusive methods, teachings, content, field trips and surprises along the way.

Course Objectives

• Students can build differentiation, loyalty, and long-term business value by meeting their customers’ needs and exceeding their expectations
• Students will understand the interactions people have with your company and surface problem areas and opportunities through a customer journey map.
• Students will focus their efforts by choosing the most impactful moments to design.
• Students can enable others to understand and execute their vision with a service blueprint.

Course Outline

Week 1 – Module 1

Course Orientation, Introduction to Service Design

Week 2 : Module 2

Becoming Human-Centred, Empathy Experiments, Building EQ, Empathy Mapping, Behavioural Science

Week 3 : Module 4

Developing a Creative Mindset, Lateral Thinking, Workshop Facilitation, Co-Design & Collaboration, Ideation & Brainstorming

Week 4 : Module 5,

Project Planning & Risk, Agile, Feature Prioritisation, Minimum Viable Services, Roadmaps & Kanban

Week 5 : Module 7

Analysing a Brief, Exploration Areas, Scope and Remit, Individual Project Briefing

Week 6 : Module 8,

Research Design, Sampling, Qualitative Methods, Quantitative Methods

Week 7 : Module 11

Journey / Experience Mapping, Pain Intensity Measurement, User Solutions, Impact Assessment, Opportunity Prioritisation, Service Vision Statement

Week 8 : Module 12

Brand, Equity & Value, Key Attributes, Experience Principles

Week 9 : Module 15

Physical Prototyping, Role Play & Bodystorming, Scripting & Staging, Desktop Walkthroughs, Digital Prototyping, Usability Testing

Week 10 : Module 17

Touchpoint Development, Signposting, Set Design & Flow, Evidencing,

Communications Design, Staff Onboarding, Training & Tools

Week 11 : Module 18

Service Blueprints, Service Specifications, Presenting & Storytelling

Dress Rehearsals

Text books

Stickdorn, Marc.,Schneider, Jakob. (2012) . This is Service Design Thinking: Basics, Tools, Cases. 1^st Edition. Wiley

Course Description

Describes the relationship that needs to be established between innovation, business strategy, and project management to turn a creative idea into a reality.  We will explore the importance of identifying the components of an innovative culture, existing differences, challenges, and the new set of skills needed in innovation project management

Course Objectives

•     Students can explain the links needed to bridge innovation, project management, and business strategy
• Students can describe the different types of innovation and the form of project management each require
• Students can identify the differences between traditional and innovation project management, especially regarding governance, human resources management challenges, components of an innovative culture and competencies needed by innovation project managers
• Students can establish business value and the importance of new metrics for measuring and reporting business value
• Students can relate innovation to business models and the skills needed to contribute in the business model development
• Students can recognize the roadblocks affecting innovation project management and their cause to determine what actions can be taken
• Students can determine the success and failure criteria of an innovation project

Course Outline

• Big Ambitious Ideas
• Customer Validation
• Value Proposition
• Prototyping
• Branding & Marketing
• Mentor Idea Review
• Legal & IP
• Go to Market
• Product Development
• Selling and Revenue
• Fundraising Capital
• Hiring & Onboarding
• Growth & Scaling

Text books

•   Ries, Eric (2011). The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. Currency