 students welcome to module 37 of your Introduction to Data Science course in this module we will see how is the SQL suitable for data science and what are the features and attributes that are useful for data science we have discussed these three or four basic qualities of software development and then we have seen what is Python and R studio along with them and then there are some other languages again it will depend on your expertise that the tools have their own features and qualities there are some comparison available between different tools that if you can use Python or R in data science but even if you are good in Java you can use it if you are good in Scala or maybe even C, C++, C sharp, .NET basically nothing stops you there are some things that are more easy they are more suitable for easy means but if your expertise is not there then you don't have to really spend your time in that that you develop new expertise in the language where you already have expertise you can use it to develop your skills so the architecture of the application I told you in the beginning that your application is where the user enters the data the integration layer is below that of the database so the data in the database basically you analyze the different perspectives because the data you have taken from the source system in your database or in your data science or analytics environment for that you need to integrate the database you have to integrate with your code so the SQL database that you have to integrate with the API as I told you through stored procedures you can do that you can develop different features in the connections similarly you can do the stored procedure or the view in it you can do the database integration so any ways you allow different programming languages along with that you can integrate with them then there are other features like there are built-in features statistical functions the latest versions of databases like oracle, IBM, etc. they have built-in features that you can write better code with minimum effort then there are many other features anyway as you see even as a data scientist when you go to any field when you join an industry you will understand that you have to learn a history of the company or department where you have to do your job so you might have to change some of your skills you will get something in some language in some database but you as a data scientist you will have to adapt to that environment so these are the things that help you how you have to do it and the SQL language or your structure databases we use another term that is relational database management system which I told you in the beginning that in each column in a database each column can be linked with one another like if you are a student your role number, your age, your class, your result these are all links then if there is a second table or a second database where the result is or the history of the fee payment so if it is in any other table or in any other application then you can integrate them so these are basically features that enable you so you have to be convinced that why this is useful for you for which attributes you will use as a data scientist or data science as a whole definitely we have seen that this is one of the third most required skill so as a data scientist you should know that as a data scientist in some industry or environment I should know in which situation I have to use which version and which one I have to use in my database and then which features which functions when to use where to use and why to use these are the things in which you have to develop your skill and this is what you need to develop if I am talking about Oracle database then if there is a version R12 then R17 or R19 then what is the difference is it necessary for me to go to the latest version or maybe it is good for you or your organization that you should be moderate stick to one of the older versions and then after that you should upgrade it or go to another one so these are the different features to store your data as a data scientist our requirement is that you can do it I want to query that data because query means if I want to read or understand anything in the SQL language it is called query-query-writing its commands are you will study in a different course because in this scope our core programming skills we did not have to develop our focus is to tell you everything and to build your orientation so that it is easier to understand and understand so basically when you want to store the data so the query the most difficult of the SQL the most difficult in the sense that it has to be optimized so the most used command that is select when you will understand when you go to a database and create your own data create a table create your own account so you will understand that some things are very easy some you develop along with your expertise and then what you do again and again it becomes easy for you so basically this is the thing what you have to do so these are the features that you should know that what was so special when you use the database you have to become a good data scientist now if we look at the whole so in this diagram you will see a lot of things now in this the 10-12 features that you are seeing with the query basically you have done some programming when you write the query or the syntax of any language or if you write a command in it or write a command or select a command your nested loops then you put your conditions from one level to the other from the second to the third then what if else basically if you see in life if we make the decisions all the languages whether it is SQL or any other language the syntax of it it works the same way like our brain works the way we think should I do this or not what will be the benefit what is the harm I will do this I can do this in a good way I cannot do this all the basic programming skills and overall your data science environment are all in this and as you know in personal life we say this is my past experience in the basis of this I will do future work so that is also prediction estimation in data science they do these things in their life the same principles are applicable except for the fact now we work on all the data तो भी आए एक लागता है.