What’s so important about data in this day. Age maintaining a healthy business goes hand-in-hand alongside working with data whether you understand it or not. There is no denying that data is that the foundation of any successful company and the business entrepreneurs that are leading the way are aware that looking deeper into data is what will make them tower above the competition.
Data team: They will want to solve a business problem. The team will do a significant amount of work on the data that is available first based on that.
Business intelligence team: The team will provide a business insights dashboard.
Data science team: After the dashboard is ready. The team will use some business analytics or data analytics tools to develop models that could predict future outcomes.
順序：Data team ----> Business intelligence team ----> Data science team
The confusion #1:
The constant evolution of the data science industry.
For example, someone who had the title of Statistician twenty five years ago would have been responsible for gathering and cleaning data sets and applying various statistical methods to the data. After some years, however, with the growth of data and the radical improvement of technology this statistician would now be required to extract patterns from data henceforth a new buzzword was coined.
Data Mining similary forward wind a few more years in the same statistican due to new mathematical and statistical models could now perform more accurate forecasts.
Predictive analytics who is more qualified now to be part of the statistics department predictive analytics team. Or have the title Data Scientist.
說了這麼多，有可能一些同學會看頭暈，因爲不知道順序怎麼樣。
順序其實這樣的：
Statistics -> Data mining -> Predictive analytics -> Data Scientist.
下面講一下各個的職能：
Statistics: Gathering and cleaning data sets and applying various statistical methods to the data.
Data mining: Use new mathematical and statistical models to perform mroe accurate forecasts.
Predictive analytics: Who is more qualified now to be part of the statistics department predictive analytics team. Or has the title data scientist.
The confusion #2:
HR managers
This causes HR label job positions inaccurately often seeming like they are choosing them on a whim.
The confusion #3:
The increasing number of data science terms in the data science glossary.
Data Science jobs:
Business analytics, data analytics, data science, business intelligence, machine learning.
這個總結不是那麼多，如果有哪裏不對，就指出來哦。博主會虛心接受的。
