This module aims to:

Develop students' advanced understanding of Big Data and its significance in shaping entrepreneurial strategies in modern business;

Enhance students' ability to source and integrate diverse data types to address complex, real-world business challenges through evidence-based decision-making;

Provide comprehensive knowledge of statistical concepts and their application in high-level data analysis;

Build proficiency in essential workplace software and digital tools, such as Python and Excel, tailored to solving data-centric business problems;

Empower students with the analytical skills required to interpret and draw meaningful insights from macroeconomic and firm-level data, supporting strategic business decisions;

Instil an understanding of the ethical considerations involved in data collection, analysis, and interpretation, promoting integrity and responsibility in data-driven entrepreneurial practices.

(LO1) Students will be able to analyse entrepreneurial settings and appropriately use advanced digital tools and software, including Bloomberg Terminals, Excel, and Python, for data analysis.

(LO2) Students will be able to assemble and apply up-to-date quantitative methods for data collection and analysis, showcasing the ability to design and execute comprehensive research projects.

(LO3) Students will be able to critically evaluate and interpret macroeconomic and firm-level quantitative data, integrating insights into strategic business planning.

(LO4) Students will be able to demonstrate the ability to investigate implications of Big Data in the business sector, recognising its transformative potential.

(LO5) Students will be able to critically analyse and assess how experts across private, public, and international organisations use data-driven approaches to solve complex business and societal challenges, illustrating the interplay between data and decision-making.

(S1) Numeracy
The module will enable students to demonstrate strong numeracy skills and the ability to analyse quantitative data accurately.

(S2) IT skills
The module will enable students to exhibit high-level IT proficiency, including the use of advanced software for data analysis and business applications.

(S3) Commercial awareness
The module will enable students to demonstrate an enhanced sense of commercial awareness, applying business insights in practical scenarios.

(S4) Problem solving
The module will enable students to collaborate effectively as part of a team, contributing to collective problem-solving in data-driven projects.

(S5) Ethical awareness
The module will help students develop a strong sense of ethical awareness, ensuring responsible data use and analysis.

(S6) International awareness
Students will develop the necessary skills to demonstrate international awareness, understanding global data trends and their implications for business and entrepreneurship.

(S7) Teamwork
Through the group assessment component, students will be able to develop and demonstrate their skills in teamwork.

(S8) Communication skills
Through the group assessment component, students will be able to develop and demonstrate their communication skills.

(S9) Leadership
Through group work, students will develop and demonstrate their skills in leading small teams/groups on shared tasks.

(S10) Lifelong learning
This module will enable students to develop lifelong learning skills in business analytics.

Core content will be delivered through a mix of live and recorded lectures, supplemented with real-world corporate examples. Students will engage with corporate case studies to solidify their understanding of theoretical concepts. Seminars are designed to engage students actively, using a combination of group work, role-playing exercises, case simulations, and discussions based on recent business news.

Lectures:

Synchronous x 8 hours (2 hours x 4)
Asynchronous x 12 hours (2 hours x 6)

Seminars x 6 hours (1 hour x 6)

Self-directed learning x 174 hours

Students will reflect on contemporary issues and prepare for lectures and seminars with asynchronous material.

Fundamentals of Big Data and Statistical Concepts:
Introduction to Big Data and its significance in entrepreneurship.
Core statistical concepts (regression analysis, descriptive statistics, correlation analysis).

Introduction to Essential Software and Programming for Entrepreneurs:
Mastering Python for data analysis, visualisation, and basic programming skills.

Applications of Big Data in Entrepreneurship:
Harnessing mobile phone data and social media insights for business strategy.
Utilising satellite imagery for market research and economic trend identification.
Risk and uncertainty management using data analytics.
Textual content analysis for market intelligence and consumer insights.

Mapping and understanding the global supply chain network.

Business Ethics in the Digital Age:
Ethical considerations in data collection, analysis, and usage.
Responsible use of data in entrepreneurship.
Case studies highlighting data e thics and their implications in business.