Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

March 11, 2014

Data Science Challenge

Filed under: Challenges,Cloudera,Hadoop,MapReduce — Patrick Durusau @ 4:22 pm

Data Science Challenge

Some details from the registration page:

Prerequisite: Data Science Essentials (DS-200)
Schedule: Twice per year
Duration: Three months from launch date
Next Challenge Date: March 31, 2014
Language: English
Price: USD $600

From the webpage:

Cloudera will release a Data Science Challenge twice each year. Each bi-quarterly project is based on a real-world data science problem involving a large data set and is open to candidates for three months to complete. During the open period, candidates may work on their project individually and at their own pace.

Current Data Science Challenge

The new Data Science Challenge: Detecting Anomalies in Medicare Claims will be available starting March 31, 2014, and will cost USD $600.

In the U.S., Medicare reimburses private providers for medical procedures performed for covered individuals. As such, it needs to verify that the type of procedures performed and the cost of those procedures are consistent and reasonable. Finally, it needs to detect possible errors or fraud in claims for reimbursement from providers. You have been hired to analyze a large amount of data from Medicare and try to detect abnormal data — providers, areas, or patients with unusual procedures and/or claims.

Register for the challenge.

Build a Winning Model

CCP candidates compete against each other and against a benchmark set by a committee including some of the world’s elite data scientists. Participants who surpass evaluation benchmarks receive the CCP: Data Scientist credential.

Lead the Field

Those with the highest scores from each Challenge will have an opportunity to share their solutions and promote their work on cloudera.com and via press and social media outlets. All candidates retain the full rights to their own work and may leverage their models outside of the Challenge as they choose.

Useful way to develop some street cred in data science.

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