Advanced Business Analytics

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Goal: The purpose of this course is to familiarize students with the methods of making data-driven decisions that can enhance business performance and results. The course focuses on achieving the following objectives:

• utilizing tools and techniques to investigate and analyze data.

• comprehending patterns and relationships within the data.

• summarizing the data in a manner that is meaningful and informative.

• presenting insights in a clear and actionable manner for stakeholders.

Ultimately, the Advanced Business Analytics course aims to empower decision-makers within an organization to make well-informed choices based on data-driven insights, resulting in improved business performance and outcomes.

Textbooks:
(1) Mike X Cohen (2021), Linear Algebra: Theory, Intuition, Code, Sincxpress. [Link]
(2) Prince (2018), Predictive Analytics for Business Strategy, McGraw Hill. [Link]
(3) Békés and Kézdi (2021), Data Analysis for Business, Economics, and Policy, Cambridge University Press. [Link]
(4) James et al. (2021), An Introduction to Statistical Learning (2nd Edition), Springer. [Link]

Schedule:
Part I - Linear Algebra for Everyone

• vectors and matrices

• solving linear equations

• four fundamental subspaces

• orthogonality

• eigenvalues and eigenvectors

• singular value decomposition

Part 2 - Linear Regressions 

• A LOT of case studies

• How to present results

Part 3 - Dimension Reduction

• LASSO regression

• Ridge regression

• Principal component analysis

Part 4 - Experimental Design

• Randomized experiments and potential outcomes

• Field experiments, A/B testing, survey experiments

• Case studies

Supplementary material:

• The Difference Between Business Analytics and Data Science by UT Dallas Business School [Link]

• Linear Algebra for Everyone by Gilbert Strang [Link] 

• Economists at Amazon [Link]

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