To my undergraduate (and some graduate) followers: Team up with friends. Turn one weekend into a resume-worthy project

· Source: Mike Talks AI · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning · Depth: Novice, quick

Summary

The article highlights three upcoming academic competitions designed to help undergraduate and graduate students build skills and enhance their resumes. The first is COMAP's Mathematical Contest in Modeling (MCM), an international competition for undergraduates running from January 29th to February 2nd, which involves solving challenging Industrial Engineering/Operations Research problems over one long weekend. The second is Manhattan University's Business Analytics Competition, an in-person undergraduate event in NYC offering a $5,000 first-place prize. Finally, The University of South Carolina (USC) Big Data Health Science Student Case Competition, held virtually from February 6-8, targets seniors and graduate students, challenging 30 teams to analyze a dataset within 24 hours for a $5,000 first-place prize, focusing on analytical, teamwork, and presentation skills in health science.

Key takeaway

For undergraduate and graduate students seeking to enhance their resumes and practical skills, actively participating in academic competitions like the COMAP MCM or USC's Big Data Health Science Case Competition is highly recommended. These events offer invaluable hands-on experience in problem-solving, teamwork, and presentation, directly applicable to future careers. Consider forming a team with peers to tackle these challenges and gain a competitive edge.

Key insights

Academic competitions offer students practical skill development and resume-building opportunities.

Principles

Method

Students form teams, select a problem, analyze data, develop solutions, and present findings to judges, often under time constraints.

In practice

Topics

Best for: AI Student, Data Scientist, Business Analyst

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Editorial summary, takeaway, and curation by AIssential. Original article published by Mike Talks AI.