CLEANING DATA 中文是什么意思 - 中文翻译

• 清理数据
• 清洁数据
• 清洗数据
• 整理数据

在 英语 中使用 Cleaning data 的示例及其翻译为 中文

Filtering and cleaning data.
过滤和清洁数据.

It shouldn't be messy, because you don't want to spend a lot of time cleaning data.
数据集不应混乱，因为你不希望花费大量时间清理数据。

Collecting and cleaning data typically makes up around 80 percent of a data scientist's work.
收集和清理数据通常占了数据科学家工作的80%左右。

Of analyst time is spent cleaning data.
数据分析80%的时间都花在了数据清洗上.

However, cleaning data at this stage does nothing to prevent the entry of bad data in the future.
不过，在此阶段清理数据并不会防止劣质数据在将来进入其中。

Data analysts spend most of their time cleaning data.
结果，数据科学家花费了大部分时间来清理数据。

IoT technology can transmit cleaning data and inventory levels in real-time, enabling management to deploy critical resources efficiently.
物联网技术可以实时显示清洁数据和库存水平，从而使管理人员能够有效地部署关键资源。

It shouldn't be messy, because you don't want to spend a lot of time cleaning data.
数据集不能是混乱的，因为你不希望花费大量时间整理数据。

If you can show that you're experienced at cleaning data, you will immediately be more valuable.
如果你可以证明你在数据清理方面经验丰富，那么你将立即变得更有价值。

It shouldn't be messy, because you don't want to spend a lot of time cleaning data.
数据集不应该是混乱的，因为你不希望花费大量时间来清理数据。

Abilities that are important are gathering and cleaning data, building systems, and developing and supporting algorithms and other statistical engines.
重要的能力是收集和清理数据、构建系统，以及开发和支持算法和其他统计引擎。

The old saying about spending 70-80% of your time just collecting and cleaning data is true.
老话说：需要花费70-80％的时间来收集和清理数据，这是真实的。

Rajpurohit says he spends a lot of his energy cleaning data, but also researching.
Rajpurohit说，他花了很多精力来清洁数据和做研究。

A high-quality data set should not be messy, because you do not want to spend a lot of time cleaning data.
数据集不应混乱，因为你不希望花费大量时间清理数据。

Data engineers are responsible for maintaining the data pipeline: ingesting data, cleaning data, feature engineering, and model discovery.
数据工程师负责维护数据管线：提取数据，清理数据，特征工程和模型发现。

Big data analytics are useful in finding existing insights and creating connections between data points and sets, as well as cleaning data.
大数据分析有助于整合现有的见解，在数据点和数据集之间建立联系，并清理数据。

In fact, a lot of data scientists argue that the initial steps of obtaining and cleaning data constitute 80% of the job.
事实上，很多数据科学家声称开始获取和清洗数据的工作量要占整个工作的80%。

A high-quality data set should not be messy, because you do not want to spend a lot of time cleaning data.
数据集不应该是混乱的，因为你不希望花费大量时间来清理数据。

The old saying about spending 70-80% of your time just collecting and cleaning data is true.
俗话说得好，“花费你70-80%的时间，收集、清洗数据”是一点也不为过的。

The desktop edition has a free community edition that's missing two of the most attractive features: TurboPrep for cleaning data and AutoModel for generating results.
桌面版有一个免费的社区版，但它缺少两个最吸引人的特性:用于清理数据的TurboPrep和用于生成结果的AutoModel。

Issue:“Clean data” is a matter of perspective.
问题：“清理数据”是一个视角问题.

By doing this, they can filter and clean data.
通过这样做，他们可以过滤和清理数据。

By doing this, they can filter and clean data.
通过这样做，可以过滤和清理数据。

There are no clean data sets.”.
压根儿就没有干净的数据集。

Good analysis rests on clean data- it's as simple as that.
良好的分析依赖于干净的数据-就这么简单。

The MR CLEAN Data.
干净的数据》.

All downstream analyses were based on high-quality clean data.
所有后续分析都给予高质量的cleandata。