Examples of using Many data in English and their translations into Chinese
{-}
-
Political
-
Ecclesiastic
-
Programming
This task uses the same database in many different ways and requires the development of many data mining techniques.
There are many data centres, and many factors(such as geographic location and search traffic) determine where a query is sent.
The challenge is that you can't ignore data gravity, as many data sources still live at the edge or in the enterprise.
With the growing focus on power costs, many data centers are reducing their reliance on chillers to improve the energy efficiency of their facilities.
These applications process many data types including images, day and night video, multi-and-hyper spectral imagery, and Light Detection and Ranging(LIDAR).
Many data stories also state or imply a causal relationship between two variables, but cause is a tricky thing and easily misunderstood.
Many data storage technologies cannot support the required level of scalability, performance, and maintenance, while keeping support costs low.
Many data lakes are being used for data whose privacy and regulatory requirements are likely to represent risk exposure.
Many data scientists have their own website which serves as both a repository of their work and a blog of their thoughts.
Therefore, many data center is still in excess, will need to upgrade“.
Many Data Warehousing applications are compatible with SQL-based querying languages, and Hive supports the portability and transfer of SQL-based data to Hadoop.
Many data science resources incorporate statistical methods but lack a deeper statistical perspective.
The challenge is that you can't ignore data gravity, as many data sources still live at the edge or in the enterprise.
When we find a simple model that seems to explain many data points we are tempted to shout"Eureka!".
The company says that changing the way in which computer memory works could improve many data operations- not just big data or database searches.
Many data structures can indeed have two levels of reference counting, when there are users of different“classes”.
In my opinion, the fact that expectation does not match reality is the ultimate reason why many data scientists leave.
I have taken many data science-related courses and audited portions of many more.
XGBoost provides a parallel tree boosting(also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
Pandas is built on Top of numpy, thereby preserving fast execution speed and offering many data engineering features, including.