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What is DataSource.ai 2.0?
DataSource.ai 2.0 is an innovative platform that caters to data scientists who want to enhance their skills. With its user-friendly interface, this solution offers easy access to diverse data sets and the opportunity to participate in data science tournaments. Users can put their skills to the test by solving complex real-world problems and collaborating with fellow data scientists. DataSource.ai 2.0 aims to provide an enriching and challenging data science experience. Its intuitive interface allows users to swiftly find data sets, build models, and submit solutions. The platform offers a wide range of data science problems, including time-series forecasting, image classification, and natural language processing. Participants can compete against their peers for prizes and recognition, empowering them to elevate their data science expertise.
Information
- Price
- Free
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Website traffic
- Monthly visits14.62K
- Avg visit duration00:02:39
- Bounce rate76.30%
- Unique users--
- Total pages views40.27K
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DataSource.ai 2.0 FQA
- What is the minimum contribution to participate?
- What do I get as compensation for my money?
- How does DataSource.ai choose the problems for the tournament?
- How complex are the tournament problems?
- How often are tournaments run?
DataSource.ai 2.0 Use Cases
Test your competitive spirit, your love of data science, and enjoy the adrenaline rush as you prove (and enhance) your data skills.
Join Tournament and compete in data science competitions.
Explore Tournament and participate in community-funded data science tournaments.
Join the tournament with an entry fee of $USD 10 and support the tournament and the community.
Learn applied Machine Learning, stoke your competitive spirit, and compare your skills with other data scientists.
Receive the Machine Learning models of the winners and have a chance to win the final prize.
Participate individually in the tournaments and keep things simple.
Challenge your friends and colleagues to join the tournament and learn as a group.
Get access to release datasets and re-train your model with new observations.
Ensure transparency in every stage of the tournament and learn from the best.
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