UCL School of Management

Module Fact Sheet

MSIN0093: Business Strategy and Analytics

Taught by
Level
M-level (4th year undergraduate)
Prerequisites
None
Eligibility
4th year IMB, 4th year Management Science, Leuven affiliates
Terms
1
Delivery method
10 x 3-hour sessions
Assessment
Individual Coursework 60% and Group Coursework 40%
Previous Module Code
MSINM051

Course overview

Data is becoming increasingly integrated and important to many traditional and entrepreneurial ventures across industries. Data is providing unprecedented insight into a firm’s performance, informing questions about competition, customers, profitability, and more. Understanding how data can be leveraged to improve an organization’s strategy and performance has become an essential skill for managers at all levels and areas of the organization.

In this module, students will examine data broadly in business contexts and how it is utilized within the firm by studying foundational strategy, data-based business models, and platform and entrepreneurial strategy. Students will consider how data is used to develop and evaluate strategic decisions. Analytics approaches that complement strategic decision-making will be covered, including experiments and some “quasi-experimental” methods.

Learning outcomes

Upon successful completion of the module, students will be able to:

  • Understand fundamental frameworks of strategy and business models
  • Apply ideas from the economics of information to data-based products
  • Understand the principles of using data to describe, explain, and predict outcomes of interest
  • Frame business and strategy questions and apply data to those questions
  • Consider the implications of data on the management, culture, and organization of the firm
  • Organize and present arguments using data

Topics covered

Strategy topics to be covered include:

  • Product market competition and the 5(+1) competitive forces
  • Resources and capabilities of the organization
  • Strategy dynamics
  • Economics of data-based products
  • Ethics, privacy considerations, and security of data

Data topics to be covered include:

  • Summarization and visualization
  • Regression
  • Correlation and causation in regression
  • Endogeneity
  • Randomized control trials
  • Causal identification of observational data
  • Prediction models

Assessment summary

Individual Coursework 60% and Group Coursework 40%

Current students should refer to Moodle for specific details of the current year’s assessment.

Past versions of this module

MSIN0093 18/19

MSINM051 17/18

Last updated Wednesday, 26 June 2019