You can call it business or data hacking, but the idea is to use analytic intelligence to reverse-engineer algorithms, transform, manipulate and modify data in external databases, without even accessing the databases in questions, for your business advantage. Should data scientists learn these data attack techniques? I believe so, in order to outsmart data hackers.
With more data flowing in from various sectors of businesses and industries, it becomes imperative to adopt new age technologies, like Data Science, to store, analyze, and process such data. But, to understand what is Data Science, it's meaning and it's definition, let us first understand what "data" is. Data is the set of quantitative and qualitative variables that can or cannot be translated into an efficient form for processing. Data can be: Structured – the data is organized in patterns and can be easily searched from relative databases. Example of structured data can be an employee database where the data is organized in a standardized format.
Often the words data and database are used incorrectly. The two words are similar, but different. In data science, it is important to understand and use the words uniquely without mistakes. Using any app to write, there is auto completion, an AI based helper to quickly finish our document, email, etc. This is the easiest way the words will get mixed up.
Oracle 12c is the latest in a long line of Oracle products. After struggling to match three versions of Oracle 11 OCCI drivers and my client's various databases, I thought that it would just be easier in general if I got a good look at where Oracle was going. This book seemed to be the answer. It has a very good top level introduction to 12c, but it never descends far into the particulars to give you an understanding of the differences. I think that with only 380 pages of working text that that was a good decision.
Data Science is not just about data. The bare basics are recognizing what all data to keep, identifying how to process it for different results. It does not stop there. Data scientists need to figure out blanks in data and fill them with data that'may' come up in future. Data Science essentially is about connecting dots in businesses and using existing and non-existing data to meet the demands of each business.