3.Now when we get to the third and fourth moments, things get a little bit trickier, but they're still concepts that are easy to grasp. The third moment is called skew, and it is basically a measure of how lopsided a distribution is.
@@ -807,7 +807,7 @@ p2 2 RAVENA COEYMANS SELKIRK CENTRAL SCHOOL DISTRICT ALBANY
有许多聚合操作，例如平均值，总和等，您希望在数字字段上执行这些操作。 这些是执行它的方法：
***Average**: To find out the average number of students in the `ELEMENTARY` school who are obese, we'll first filter the `ELEMENTARY` data with the following command:
***Data is your most valuable resource**: You need a proper data strategy to make sure data scientists have easy access to the curated contents they need. Properly classifying the data, set appropriate governance policies, and make the metadata searchable will reduce the time data scientists spend acquiring the data and then asking for permission to use it. This will not only increase their productivity, it will also improve their job satisfaction as they will spend more time working on doing actual data science.
***Services**: Every architect planning for data science should be thinking about a **service-oriented architecture** (**SOA**). Contrary to traditional monolithic applications where all the features are bundled together into a single deployment, a service-oriented system breaks down functionalities into services which are designed to do a few things but to do it very well, with high performance and scalability. These systems are then deployed and maintained independently from each other giving scalability and reliability to the whole application infrastructure. For example, you could have a service that runs algorithms to create a deep learning model, another one would persist the models and let applications run it to make predictions on customer data, and so on.
***Tools do matter!** Without the proper tools, some tasks become extremely difficult to complete (at least that's the rationale I use to explain why I fail at fixing stuff around the house). However, you also want to keep the tools simple, standardized, and reasonably integrated so they can be used by less skilled users (even if I was given the right tool, I'm not sure I would have been able to complete the house fixing task unless it's simple enough to use). Once you decrease the learning curve to use these tools, non-data scientist users will feel more comfortable using them.
