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- Teaching & the Case Method
最新個案
- A practical guide to SEC ï¬nancial reporting and disclosures for successful regulatory crowdfunding
- Quality shareholders versus transient investors: The alarming case of product recalls
- The Health Equity Accelerator at Boston Medical Center
- Monosha Biotech: Growth Challenges of a Social Enterprise Brand
- Assessing the Value of Unifying and De-duplicating Customer Data, Spreadsheet Supplement
- Building an AI First Snack Company: A Hands-on Generative AI Exercise, Data Supplement
- Building an AI First Snack Company: A Hands-on Generative AI Exercise
- Board Director Dilemmas: The Tradeoffs of Board Selection
- Barbie: Reviving a Cultural Icon at Mattel (Abridged)
- Happiness Capital: A Hundred-Year-Old Family Business's Quest to Create Happiness
Data Analytics and the Overall Research Process
內容大綱
In many cases, data analysts are given the question to answer or the data set to be analyzed. It's important, however, to understand that there are numerous steps, before and after the empirical analysis is done, that define an overall rigorous scientific process. This note outlines the basic steps that are important in asking an empirical research question, answering it, and presenting findings. The process described in the note generalizes to any empirical research domain. Given the rising prominence of data analytics in sports, we illustrate the process here using a question that is often discussed by basketball observers: Does calling a timeout in basketball end a run being made by the opposing team? At Darden, this note is used in the MBA and Executive MBA class "Data Analytics and Leadership Judgment in Sports"; it would also be suitable in many data analytics courses or a module in any course introducing the basics of an empirical research process.