 during this discussion of comparison of python with different other languages next comparison is that is with C++ C++ is sort of a lower level language but not assembly language machine language is also called as machine learning and machine language are two different things machine learning is a technique which we use in data science using python or any other scripting language but machine language is a language which is your hardware when you do any work on a computer ultimately the work below that is at the lowest level, the physical level whatever language is close to it is faster than all if you want to note one more thing OSI model it will help you to understand lot of things application layer, recession layer, IP layer and physical layer, there are 7 layers so if you will talk on the lower level you mean between two computer systems if you are doing the process your code will be the same as your performance speed the other pros and cons are even the consumption of electricity your microprocessor chips they consume a lot of power so when you go on a large scale, enterprise level where you know how long your computer will run and how much electricity will be spent because you have to pay the bill so all these things you have to do to that level so step by step you will learn that there is nothing in the isolation it is a very big ecosystem it has information and communication technologies and data science is one component of it programming is one component of it so when the architecture of software or the solution design they take care of this stuff as well so OSI model when you study it will definitely leave it will shift in your understanding what all things are involved in it and how they are handled on different levels so OSI and Python basically as I said it is the same as Java and if you look at it because we are studying data science and our objective is to study Python so that is why we are comparing it so that you can feel comfortable that this is the right thing to start if there is a comparison technically speaking if there is a situation if we want to do software development in C++ or we want to do it in Java then that comparison is easy or it can have a different objective but here also because our focus is Python or data science so that is why we are comparing it to Python so this is the easiest thing beginners right again as we talked about in Java of course there is a lack of salary because it has more demand and popularity so it has more developers but it doesn't mean that if you have learnt C++ or you know C++ then you will be left behind there is no such scene because it is their own market because we are comparing it from the point of view of data science from the point of view of analytics so maybe you are thinking this is their own market and I will tell you that that language there is something called Koball C-O-B-O-L this was the first language which I learnt back in 1984 right before I started my master's degree and maybe many of you haven't even been born yet and this language is a common business oriented language this language was very popular in banking and there are still applications which are going on in Koball in the world even if you have a connection in the banking industry you ask them and they will tell you that they still have software that is going on in Koball so the purpose of telling is that any knowledge has its own importance has its own market value to discuss or make a commercial comparison but because these on the fly things were available so we are sharing these aspects with you as well web development for web development is not necessary because C++ is used for low level programming ETL is one thing if we haven't discussed it yet we will do ETL extract transform and load maybe in data wrangling we have discussed so in this we have discussed it in detail in data wrangling in this particular thing that why do you need speed so this also has a limitation there are many other things but make sure for you the best thing to start with is python programming language these are their advantages disadvantages we have compared with a different angle the important thing this is very very critical to understand its speed and what I was talking about is that it works on low level that is axion machine okay we are definitely not comparing libraries here but these two things they give a lot of weight or value a practical thing what we have done practically speaking in the industry in python the statistical model to develop is relatively easy and this what the people in the industry do and this is a good practice they do that one statistical model they develop in python they train the model when you study machine learning and artificial intelligence then you will understand to select the model train it then do analysis on it test data then you will learn these things but for now it is most important for you to understand because in python the statistical model to develop is easy what we do even we do it in our professional life if in the telecom industry I know that I have a lot of data in the model in the model there can be improvement in the statistical modeling we have to make sure that we select the model and adopt it then we minimize those limitations so that you have the advantage when you do so many efforts to train the model correct it put the effort on python so that you can easily do this once that is done now your model is tested that the source data will be the same as I gave the telecom example or ATM data or online payment system data where lakhs of transactions are happening in real time you have to control them in some way so speed and this exe on machine this makes it very very speedy now that is done in python you have developed the model you have trained it then your model is tested then you can convert it in C++ and then you deploy it in the production environment and then that is how you can take advantage of your knowledge of both languages