UCL School of Management

Module Fact Sheet

MSIN0093: Business Strategy and Analytics

Taught by
Masters, level 7
MSc Business Analytics (MS); compulsory MSc BA (CS)
Delivery method
10 x 3-hour sessions
Individual Coursework 60% and Group Coursework 40%
Previous Module Code

Course overview

Data is becoming increasingly integrated and important to many entrepreneurial ventures as well as traditional firms in nearly every industry including finance, energy, manufacturing, and healthcare. 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. Students will study strategy formation and execution and how data is used to develop and evaluate strategic decisions. Factors within and outside the firm that affect long-term performance will be examined. Business models based on data-based products will also be considered by covering the economics of data as well as ethics, privacy, and security considerations.

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

MSING051 17/18

MSING051 16/17

Last updated Friday, 17 August 2018