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  3. Data Analysis

Data Analysis (BUSA90060)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

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Contemporary business is awash in data. Modern business processes and activities usually involve multiple streams of data from areas as diverse as marketing activities, operational processes and financial activities. Therefore, managers are frequently confronted with how to harness these to understand their business better, so that they can make more informed decisions. This subject provides the fundamental quantitative skills necessary for an MBA student to extract information from data, through quantitative analysis, to make better managerial decisions. Students will be familiarized with the tools of quantitative analysis, develop the necessary skills for analytical thinking and a quantitative mind set in measuring performance. The fundamental quantitative skills from this subject provide a foundation to the advanced subjects within the MBA and provide students an analytical framework towards solving managerial problems later in their career.

Intended learning outcomes

On completion of this subject, students should be able to:



  • Analyse and summarise multivariate data clearly using Excel
  • Apply the principles of statistical variation in data analysis.
  • Select appropriate performance metrics based on statistical principles
  • Combine multiple performance metrics quantitatively.
  • Distinguish between correlation and causation in statistical analyses
  • Construct relevant statistical models from ambiguous business problems
  • Undertake regression analysis to quantify complex relationships between multiple explanatory variables and a response variable
  • Identify elements from regression output that are directly relevant to a business problem or question.
  • Identify and model nonlinear effects and interactions in regression models
  • Measure and articulate statistically significant relationships between variables
  • Apply quantitative methods and analyses to identify optimal decision strategies and risks
  • Evaluate the robustness and appreciate the limitations of data analyses

Last updated: 19 September 2024