Business Analytics MSc

Program Overview

The MSc Business Analytics program at London equips individuals with contemporary analytics skills for data-driven businesses. With no prior experience required, the program emphasizes technology-aided learning, academic rigor, and experiential learning through industry-sponsored projects. Graduates pursue diverse roles as business or data analysts in various industries, supported by career guidance and employer events.

Program Outline

MSc Business Analytics

Degree Overview

This program is designed for individuals aiming to enhance their careers in data-driven businesses by acquiring contemporary analytics skills. The program emphasizes technology-aided learning, academic rigor, and experiential learning.

Key features:

• No prior experience in analytics or technology enablers is required.
• Pre-courses equip students with the necessary skills like Python and R Programming before the start of the program.
• Requires an upper second class degree or equivalent in quantitative subjects like accounting, biology, business, computer science, economics, engineering, finance, etc.

• Learn contemporary analytics skills with a balance of academic rigor and industry practice.
• Participate in industry-sponsored summer projects to gain real-world experience.
• Understand how real-life business problems are tackled with analytics across various sectors.

Course objectives:

• Develop comprehensive contemporary skills and positive attributes to become a successful business analyst.
• Master specialist skills and technical skills, data science and data analytics, and technology skills relevant for influencing and leading organizations.
• Hone soft skills for communication and effective persuasion.
• Benefit from experiential learning through industry-sponsored projects that match your skills and interests.
• Gain hands-on real-life work experience through dissertation projects based on industry needs.

Outline

Course structure:

• Two compulsory induction weeks covering refresher courses, careers service introduction, and careers fair.
• Three pre-study online modules to ensure all students have the required background:
• Professional Ethics and Good Academic Practice
• Introduction to Python Programming
• Introduction to R Programming
• Three terms with core and elective modules.
• Applied research project in term three to integrate learning and apply it to a practical problem.

Term 1:

• Network Analytics: explores modern network science frameworks and algorithms to analyze network data.
Focuses on gaining practical skills using Python.
• Data Visualisation: introduces principles, frameworks, and techniques for visualizing data effectively.
Focuses on different approaches and audience considerations.
• Revenue Management and Pricing: delves into managing pricing and product availability for optimal organizational performance and profitability.
Covers capacity allocation, markdown management, e-commerce pricing, and demand forecasts.
• Analytics Methods for Business: equips students with standard analytical methods used for analyzing and communicating data-driven recommendations.
Focuses on practical applications using R programming.

Term 2:

• Applied Deep Learning: provides hands-on experience in Deep Learning through real-world examples across sectors.
Exposes students to applications like recommender systems, fraud detection, digital marketing, and more. Emphasizes learning by doing rather than building a strong theoretical foundation.
• Machine Learning: introduces various machine learning techniques used for data analysis and predictive modeling.
Focuses on gaining practical insights into machine learning tools using R and Python.
• Strategic Business Analytics: teaches how to design, validate, and communicate business strategies using quantitative techniques learned in other core modules.
Provides a strategic consulting approach through case studies showcasing the scope of modern business analytics.
• Digital Technologies and Value Creation: explores how digital technologies enhance business opportunities for firms.
Uses real-life case studies from sectors like MarTech, People Analytics, and Social Media Analytics. Primarily focuses on familiarization with contemporary business analytics applications for the summer research project.

Term 3:

• Applied research project: individually conducted research on a chosen topic relevant to the field.
Requires a report of 3,000-5,000 words summarizing and critically evaluating the methodology and findings.
• Four elective modules: Students choose four electives from a range of options, including:
• Applied Machine Learning
• Applied Natural Language Processing
• Business Intelligence Deployment
• Data Management Systems
• Fintech - Financial Services in the Digital World
• Practicing Management in the Digital Age

Assessment

• Coursework
• Examinations
• Presentations
• Group work
• Problem sets
• Assessed essays

Careers

Graduates of the program can pursue diverse roles as business analysts or data analysts across various industries. Some work in consulting firms, financial institutions, banks, and technology companies.

Career support:

• Career team offers guidance from the beginning, with induction sessions, careers fairs, workshops, and employer events.
• Employer events connect students with potential recruiters, including Accenture, British Airways, Ekimetrics, and EY.

Recent graduate positions:

• Digital Personalisation Specialist
• Data Analyst
• Technology Architect
• Business Integration and Architecture Analyst
• Data Scientist
• Analytics Consultant

Other

International location:

The program takes place in London, a leading global city offering students a vibrant educational and cultural experience.

Industry-sponsored projects:

The program provides students with valuable opportunities

Tuition Fees and Payment Information:

UK/Home fee September 2024 entry £21,000 Tuition fees are subject to annual change. International fee September 2024 entry £31,500 Tuition fees are subject to annual change. Deposit: £2,000 (usually paid within 1 month of receiving offer and non-refundable unless conditions of offer are not met).First installment: Half fees less deposit (payable during on-line registration which should be completed at least 5 days before the start of the induction period).Second installment: Half fees (paid in January following start of course).City's Fee status assessment policy.