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  • MSc
  • Data Driven Management

Data Driven Management

Course Description

This course will provide tools and practice for using data and models to obtain managerial insights. It will introduce quantitative methods for decision analysis including probability, linear regression, simulation, optimization, and the combination of these methods. However, analytics is not merely a collection of tools or techniques. It is a refined style of business decision analysis that enables systematic assessment of the business problem, identifying key factors which influence business outcomes, explicit evaluation of assumptions, and quantitative evaluation of uncertainty. This course provides a systematic framework for the use of data and models in managerial decisions in the fields of marketing, operations, finance, accounting, and strategy. 

Throughout the course, students will learn how to identify the type of analysis best-suited for each class of decision problem. Emphasis will be placed on leveraging business analytics tools and techniques to maximize the efficiency and effectiveness of a company’s value-added activities. Throughout the course, students will be encouraged to use and combine different tools in their analysis of corporate problems, assess the influence of uncertainty on their recommendations, and learn to communicate information from quantitative analysis including the communication of uncertainty.

Learning Outcomes

Students will be able to gain basic Excel skills and learn about different analytical models. Particularly the students will learn to:

  • Develop models and model building;
  • Perform and critically evaluate single variable and multi-variable linear regression using continuous and categorical independent variables; interpret regression coefficients; interpret regression model uncertainty; conduct diagnostic analysis and judge suitability of the model;
  • Use a linear regression model to develop recommendations and forecasts;
  • Represent sources of uncertainty with probability distributions;
  • Construct a simulation model; validate the model;
  • Use a simulation model to generate a risk profile or distribution in expected values;
  • Develop reasoned recommendations incorporating uncertainty;
  • Identify decision criteria, decision variables, objective function, and decision constraints for a set of related strategic business decisions;
  • Use Excel Solver to identify optimal decision values;
  • Interpret optimization models output and use them to evaluate strategic opportunities;
  • Determine whether an optimal solution is robust to changes in constraints or objective function coefficients; and,
  • Apply these models to various business decisions for the purpose of making better and more informed decisions.


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