3 Mistakes to Avoid While Learning Data Science | Practical Tips

 Today here we are going to talk about three such mistakes that every beginner Data Scientist makes or who is moving on to Data Scientist from some other domain,

 

he makes these three most important mistakes because of which he does not get the job. Mein bahut badak hoti hi everyone os again tak tulu w and in today's video we are going to talk about the three mistakes that every data scientist makes in the beginning stage, inside the first mistake I want to talk about Mathematics Yes, it is very big for everyone, see for Beginner Data Scientist, whenever you want to change your domain and go to data science field or you are in IT field and still you want to go to data science field. Then the biggest mistake they make is that the people of Mathematics are very much afraid of Mathematics.

And they just listen to the salary and move on to science and then from there they don't pay any attention to mathematics, inside mathematics it will be like statics, you are providity, you have permutation commission here, linear is now done. So they don't pay proper attention to those mathematics, then they focus only on more kigottens, they start applying it directly, they don't care about the max behind them, they don't take knowledge. Applying on Mathematics, after which data set you should like, it starts avoiding about all of them, due to which they face problems while moving forward because see whenever you will work for any professional work. At that time, you also need to modify the alguttam which you have read inside those algorms. So there is a lot of need to change the maths formula which is behind the maths, so this is the biggest mistake that they give less attention to maths, due to which they face a big problem. There is cash China later I will talk about critical chicking they never think about critical chicking now what is this thing but what is the logic here like whenever they start reducing on a data set then they We don't do research about what the two data sets want to say and what input and output is going to come out of it, we take two sets directly and then start applying filters, start manipulating data from them, cleaning etc. You start planting potatoes on it, they don't even take knowledge about the data, what is the data going to say, what will we do with the data, on which steps, we are not giving any knowledge about it, I think that's enough Once there is data, it is not knowledge after us, then it is not. If ta is not there, then we have to find out that what is it, as I do it in the domain and I am given the data of some medical field and it is said that it has been given by dictions and here some one Make such an app or make this kind of website so that if I have some data according to that, I will come tomorrow and see if I don't have double knowledge of medical here, so what should I do on that? I will leave it or not, it does not happen that even if you have knowledge of the medical domain, you can reduce it, but it is not that less that I will start starting it, what will happen within the time that I am in a medical field? I will go and meet the person, I will talk to him, I will ask him what this data wants to say to me, you give me a little bit of knowledge so that I can do further work on it, so basically what you have to do is go inside the particular domain and make it particular. You should go and talk to the person and confirm what that Tata is going to say to you, what has he said, in what ways to take you forward. If you want to do it in this way, do not reduce it directly on the data set, if you reduce it directly, then who knows the features which you have not included here, and if that feature is the most important feature, then it will be missed by you. Your result is that you will start getting strong dis.

And your result will start coming strong to you, then this is also a big second mistake, due to which people can bake here, now I will talk about the word which is the most important thing, date it, practice it, it is the biggest practice. It is above the project that people here do not make the right project, the problems that they face due to not making the right project are the biggest problems that they face, well, I want to tell you the real story of a company, here In the beginning, I was teaching myself, so what was my focus that I covered the course of data science as soon as possible, which was immense right, so what did I do, I completed machine learning first and then After I completed Deep Learning, then I completed Neural Network, then I completed analp, then I also completed Open, but in this I made a biggest mistake and because of which I also started passing those thoughts. I haven't billed the project, I haven't made a single project. When I was practicing, I used to get the data set as if I am taking some course, here he would have given small exams under the course, so I would have done those exams. Used to build the model on top of the data center and I used to think that ho mere ko pakka knowledge ho gaya puri has been done well so I am absolutely perfect inside this fill and I can reduce it right but when I actually I was about to go for the job, when I actually went for the job, then I came to know that the project that I have made is a basic level and everyone can do it without data site, so what is happening here tomorrow? You don't bill the project of good team, this is the biggest problem that you build the project of good, it is not that you start working on the data set you have got, no, you should research the data set, most of the people What do Iris data set which is flowery, data side starts working on it, starts working on Titanica, starts reducing on tips This is the most basic data set, on which everyone starts reducing, but now you must be studying in starting, only then you must have done it, then what did you practice in that case, what is new. You people do not know about Jain's pay and when you sit in the interview, the first question that comes after the interview is what project you have made and if you will respond to this project, I asked you on the date of flower. If it is discussed here, then whatever you say, you have the most nagging, here everyone does this and it is also different on the net, so who knows whether you have made it yourself or you have picked it up from the net, so about them There is no message and they cannot judge here, because of this one day in your career, so basically what to do, you have to pick up new data sets, there are many data sets on the website like your girl. Similar as you will search the flower data set of iris data set, then you will find four punch and data sets matching it, then you will find iris data set. Leave the center because you have put the car in the starting, then when you are going up to the project building, then what you have to do, focus on the other respected data centers and try to build your project from them. Well, I would like to tell one more thing here, if you do not even want to go through your girl's website, then the best thing here is that you go to any website and go to the top of the website and download the data from there. See about the camp, doing is also a part of data science, here the data science course that you do, swapping is also taught inside it, so if you do two crapping from there and after swapping yourself, you can use it. If you reduce the above then what will happen that you will have knowledge of two things, one is how to swap the data and when you will come with the data, it will not happen after waking up, when you will say in detail, then that Tata will be with you.

There will be absolutely raw material data, you will not know anything inside it, you will filter it completely and whereas on a website like Aap Ki Gal, if you bring the details, then you will directly get them directly here in the su file or inside the excel sheet. Will go and then you will start reducing directly, so you have to learn to reduce everything by applying date off filter, then what you should do is start doing basic scraping and then from there you bill your project, then these were the three big mistakes which Everyone does Beginner Data Scientist, because of which there is a lot of difficulty in getting his job, then you do not have to do these three big mistakes, if you are on the starting stage, these three mistakes can be improved quickly and become a very good Data Scientist.