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Data competition 数据竞赛网站整理

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一、数据竞赛:

  1. DataFountain
    地址:https://www.datafountain.cn/

  2. Biendata 地址:https://biendata.com/

  3. DataCastle
    地址: http://www.dcjingsai.com/common/cmptIndex.html

  4. 蚂蚁金融科技
    地址: https://dc.cloud.alipay.com/

  5. TinyMind 地址: https://www.tinymind.cn/

  6. 点石 地址:http://dianshi.baidu.com/

  7. 天池
    地址:https://tianchi.aliyun.com/

  8. JData 地址:https://jdata.jd.com/index.html 地址:https://jdder.jd.com

  9. Kesci 地址:https://www.kesci.com/

  10. Di-Tech算法大赛 地址:http://research.xiaojukeji.com/

  11. 全球AI挑战赛 地址:https://challenger.ai/

  12. 上海SODA大赛 地址:http://soda.shdataic.org.cn/

  13. 拍拍贷魔镜杯
    地址: https://ai.ppdai.com/mirror/show

  14. T-Brain AI实战吧
    地址: https://tbrain.trendmicro.com.tw/

  15. 工业大数据产业创新平台
    地址: http://www.industrial-bigdata.com/

  16. 讯飞 iFLYTEK A.I. 地址: http://challenge.xfyun.cn/2019/gamelist

  17. BdRace数睿思_数据挖掘竞赛平台 地址: http://www.tipdm.org/bdrace/index.html

  18. **SAS数据分析大赛 地址: http://www.saschampion.com/

  19. 长风大数据竞赛 地址: http://contest.cfdsj.cn/

  20. 数据酷客
    地址: http://cookdata.cn/competition/

  21. **健康医疗大数据产业创新大赛
    地址: https://vcbeat.net/cdhic

  22. **医疗器械创新创业大赛 地址: http://www.cmddsc.cn/

国外:

  1. Kaggle 地址: www.kaggle.com
  2. numer.ai 地址: https://numer.ai/

四、大佬的Github:

  1. 植物 :https://github.com/plantsgo
  2. wepon :https://github.com/wepe
  3. Snake:https://github.com/luoda888
  4. Drop-out:https://github.com/drop-out
  5. 金老师的知乎:https://zhuanlan.zhihu.com/jlbookworm
  6. 渣大:https://github.com/nzc
  7. 郭大:https://github.com/guoday
  8. Cortex Lab:https://github.com/CortexFoundation

五、资源整理:

六、数据集:

【开放数据集大列表】《Open Datasets | Skymind》 http://t.cn/RFAoweW

【数据集搜索引擎:Google启动新搜索引擎帮助科学家找到需要的数据集】http://t.cn/RsAHucP https://www.blog.google/products/search/making-it-easier-discover-datasets/
Dataset Search:http://t.cn/RsAHuch

【fast.ai开放数据集】“fast.ai Datasets” http://t.cn/Ezzp51m ref:《The new fast.ai research datasets collection, on AWS Open Data》 http://t.cn/EzzpXQ5 种子来了 http://t.cn/EzA7XpZ

【计算机视觉“小众”数据集集锦】《Rare Datasets for Computer Vision Every Machine Learning Expert Must Work With》 http://t.cn/EZE9Vb7

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