近年，オープンソースの機械学習ツールが充実してきたことで，機械学習を利用したデータ分析が容易となり，データサイエンティストの需要増という背景から、非専門家がデータサイエンティストとしてデータ分析に従事するケースが増えている．
このように，経験・知識が不足しているユーザが分析を行った場合，分析手順の誤りなどにより適切な分析を行うことができない場合がある．
Rating
52
Translation / English
- Posted at 24 Jan 2015 at 23:14
In recent years, open source machine learning tools have been enhanced, and data analysis using machine learning has become easier, increasing the demand for data scientist, and against this background data analysis non-experts posing as data scientists has been increasing.
A user without enough experience and knowledge, may not be able to perform a proper analysis due error in the analysis procedures.
A user without enough experience and knowledge, may not be able to perform a proper analysis due error in the analysis procedures.
Rating
50
Translation / English
Declined
- Posted at 24 Jan 2015 at 22:59
Due to full of the electrical learning tools of open source recently, it is easier to analyze the data of using electrical learning. Since the demands for data scientists are increasing,the cases that non-specialists analyze the data as data scientists are increasing.
Accordingly, If people who do not have enough experience or knowledge to analyze the data, they cannot analyze the data properly due to the mistakes of making analyze progress.
Accordingly, If people who do not have enough experience or knowledge to analyze the data, they cannot analyze the data properly due to the mistakes of making analyze progress.
Rating
49
Translation / English
- Posted at 24 Jan 2015 at 23:48
最近幾年Open Source的機械學習（Machine Leaning）工具越來越豐富，使用機械學習的方法後讓數據分析變得更容易，而且大環境中數據科學家（Data Scientist）的需求也增加了，非專業數據科學家從事數據分析的情況也變多了。這樣一來，在經驗和知識都不足的使用者分析之下，由於分析方法的錯誤等等，也是有無法正確分析的狀況產生。
★☆☆☆☆ 1.0/1
翻訳結果の質があまりにも低いため unknown declined this translation