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データサイエンスの不都合な真実

Last updated at Posted at 2015-06-15

Linkedinの記事に強く同意したのでシェアします.
(8.は必ずしも,という気もしますが)

  1. 綺麗なデータなんて無い

  2. データの準備,前処理が一番大変

  3. 95%のタスクでディープラーニングは不要

  4. 9割のケースは一般化線形モデル(シンプルなモデル)で十分

  5. ビッグデータは単なるツールの一つだ

  6. ベイズ推定を使おう

  7. どうやったか何て誰も気にしない(何をしたかだ)

  8. アカデミアとビジネスは別物

  9. (分析も大事だけど)プレゼン大事(パワポマスターになろう)

  10. 真のモデルなんてない.使えるモデルがあるだけだ

  11. 全自動分析なんてない.泥臭く手を動かそう

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