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Research has been completed on how competition can improve research performance. Companies like JPMorgan Chase also run internal contests involving large numbers of employees. [2] Examples of data science competition platforms include Bitgrit, [3] Correlation One, Kaggle, InnoCentive, Microprediction, [4] AIcrowd, [5] and Alibaba Tianchi. [6]
NYC BigApps is an annual competition sponsored by the New York City Economic Development Corporation.It provides programmers, developers, designers, and entrepreneurs with access to municipal data sets to build technological products that address civic issues affecting New York City.
The goal of the Hutter Prize is to encourage research in artificial intelligence (AI). The organizers believe that text compression and AI are equivalent problems. Hutter proved that the optimal behavior of a goal-seeking agent in an unknown but computable environment is to guess at each step that the environment is probably controlled by one of the shortest programs consistent with all ...
The M2-Competition was organized in collaboration with four companies and included six macroeconomic series, and was conducted on a real-time basis. Data was from the United States. [1] The results of the competition were published in a 1993 paper. [6] The results were claimed to be statistically identical to those of the M-Competition. [1]
The Bebras is a 45-minute multiple-choice test with 15 problems. The problems are divided into three pairs of 5, and classified as "easy", "medium" and "hard". In most countries, the competition is administered through a web system that automatically scores each participant's work.
The Global Competitiveness Report (GCR) [1] was a yearly report published by the World Economic Forum.Between 2004 and 2020, [2] the Global Competitiveness Report ranked countries based on the Global Competitiveness Index, [1] developed by Xavier Sala-i-Martin and Elsa V. Artadi. [3]
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Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.