Business Analytics
Last update: February 6, 2023
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The goal of the Business Analytics competition is to evaluate student teams’ ability to provide evidence-based business decision-making, while leveraging only those software resources widely available throughout the business world.
The contestants will be working on a prediction problem. The contestants will be provided with a context for the problem and a detailed description of the data, to include a data dictionary.
Team Composition
Teams may be made up of 1-2 people.
Schedule
Friday, March 31, 2023 7:00 - 11:00 PM
Skills and Resources
Competitive teams should have the following skills.
- The contestants must have some knowledge of predictive analytics, data mining, or machine learning techniques.
- The contestants must be able to use technology to read a relatively large dataset.
- Transform the variables as needed.
- Select the appropriate technology to approach the problem.
Teams are also expected to have foundational level knowledge of accounting, finance, economics, management, SQL, and quantitative methods.
Students may use any software, including access to the Internet, to develop and implement their model.
It is expected that contestants will access the data via a MSSQL Server that will host the database that will be utilized for the event. An IP address, username, and password will be provided at the beginning of the contest. To access the data, contestants are expected to pre-install SQL Server Management Studio (SSMS) (available for free at https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms ) or HeidiSQL ( https://www.heidisql.com/download.php ) in advance of arriving at the competition site. Contestants that desire to use Excel, R, Weka, or another tool for processing and modeling the .csv and Excel files, should have that software pre-installed as well. (For R, see https://www.r-project.org; for Weka see https://www.cs.waikato.ac.nz/ml/weka/downloading.html).
Protocol
The contestants will be provided with the context of the problem, and a detailed description of the data including a data dictionary. The research question will require the students to analyze the data for descriptive and inferential statistics, create a prediction model with final submission of a column vector of predictions in .csv format, and provide a brief report submitted as a .pdf file.
Judging will use the following criteria to determine each team’s score:
[Preliminary – will be finalized upon distribution of problem statement]
- 30% A vector of predictions for the test set. (to be used by the judges)
- 20% A justification explaining why the method/technique used is appropriate to the problem scenario.
- 20% A brief description of the method used
- 20% Inferential statistics about the data
- 10% Descriptive statistics about the data