Data Science is a scientific field that focuses on data.
Data is important in scientific fields and business fields as records or certification for a long time. Data science has been powered by the development of computer technology.
In many cases, data science is introduced with computer technologies and statistics because it is too difficult to deal with large size data without them. But the meanings and quality of the data ( meta knowledge ) are also important in data science.
The importance of the meanings and quality is clear in the case that there is one data (n =1). When measurement is difficult or the phenomenon is rare, we can get only one data. In this case, computers and statistics are not useful. But we need something to study the data. Data science could be "something."
I made the definition of data science in this page. Many people define as "Data science = Statistics + alpha". But, in my experience, Statistics is one of the part of data science.
I put a phrase, for example, "Collect Data". But each method has more abilities.
For example, to do " Data Mining ", from zero step, you do the steps "Collect Data", "Stock Data", "Link and Arrange Data" and "Find from Data".
Multi-Variable Analysis and Data Mining are part of Statistics . But I divide the block.
Strength and Weakness of Big Data
Statistical Way of Making Hypothesis
Selection of Methods
Prediction by Statistical Model
Isolation of Data, Methods and Indexes
Outlier and Missing Value
Difference from Hopes
NEXT Tools of Data Science (G7, W7, M7)Tweet