Data Analyst🤑 Roadmap😍: Conquer the Journey in 2024!👌 #DataAnalyst #Roadmap #mlwithakansha

Ladies and gentlemen, so hello everyone, welcome back to my video. In today's video, I am going to show you how you can get 10 lakhs per annum. Yeah, 10 lakhs per annum. I'm going to tell you a roadmap how you can earn 10 lakhs per annum. This is the minimum one I'm telling you. You can— you might can get like more than that even, but this is the minimum one which I'm telling to you. So let's get back on the screen and I will show you step by step how you have to do it There are multiple free resources which you can use. I would suggest you those resources even. So just watch this video till end. I'm talking about is the data analyst. So I'm going to tell you how you can be one data analyst. If you are looking for changing, uh, career, like if you are into sales, you wanted to go into data analyst or something like that, then you must watch this video completely till the end. So for being a data analyst, what do you need is you have to follow this structure which I am sharing on my screen. The first thing is the maths Stats. So you must learn some stats and math. Stats is the statistics and mathematics that we used to, you know, study in our school timing. So it was statistics and probability, which is again an easy topic to do. Linear algebra and algebra is a bit difficult, but you can do it. I know that because you don't have to learn everything because Python do have some, you know, methods or functions to do this. So I would suggest you to just go like take an overview of algebra and linear algebra and a bit of calculus and the discrete mathematics. You will need only this much of mathematics and this much of stats. Another thing that you should learn is the Excel. Excel, even using Excel only you can create something. Data analyst, first let me tell you what is the role of data analyst. Data analyst role is like You will be given data, and using that data, you have to make prediction. You have to make decisions that whether that company should invest or not on the basis of the data that they are going to provide. So new startups, I would say, they are not going to hire the data analyst. The companies like Amazon, Google, or Microsoft, Applause— there are many more companies, those who are hiring data analyst. If you will go and search Applause on the LinkedIn, you will get n number of roles for data analyst. So I would suggest you to do it quickly. It's not gonna take some like much time of yours. Like mathematics, you can do, I would say, hardly in 2 days if you are going to invest your Saturdays and Sundays on mathematics and weekdays onto this particular thing, then you can easily achieve it. Another one is the Excel. As I said, you should— you have to learn everything about Excel, I would say, because it's easier. Just upload the data, try to, you know, add like do some minus, or try to do VLOOKUP. I will add one resource which is the best resource for Excel, I would say. I have even— I myself have learned Excel from that. So I would just suggest it, and it's free of course, you don't have to pay for it even. So I would suggest you to use that resource. Yeah, you should know how to create worksheet, pivot tables, and how to format the data using basic formulas. So there are some different formulas as well. And even there is like, if you are using MS Excel, now there is something called Toolkit, which is an add-in. So I would suggest you to learn about that also because this is something magic. You will just use this and will do everything else, even only on Excel. So yeah, this is just for the basic knowledge. You are not going to use Excel, but you should know because we are— you are going to work on data. Another thing is you should learn one programming language. Either you can learn Python or you can learn R. I have learned Python. I am not into R, so that is why I have not mentioned R in this, and I don't want you guys to get any false information. So Python is something which I have learned, so I would suggest you to use the Python programming language, which is easy to learn. You have to learn some basics and like syntax and all, how to write the code in it. Which you can get onto like, uh, in my channel also I have posted some videos on syntax and like basic programming. Data structures and algorithm, you should learn data structures like I would say set or dictionary or tuples, how to use, or list. Another is the Pandas, NumPy. These two are the libraries, important library if you are going to start the data analyst. Another is the Skypi— this can be used for model creation and all. And this is for visualization. So you can create— like in Excel also we used to create graphs, so in Python also you can create graphs. After that, as I told you, you are going to work with the data. So for that purpose, you have to learn a data management tool. So what is the data management tool? There are multiple data management tools. SQL and databases, we call it. So there are something called MySQL, SQL. You can learn even others also. So DBMS normalization, ERD. So all those things related to data, like there are multiple sources and you have to combine all those data at one place. So how you are going to do it and how you're going to store this data, this is what you're going to learn in databases and SQL. Then there comes a Power BI. Now what is Power BI? Again, this is a visualization tool, easy to learn, super easy to learn, and through which you can create— like using the Python you were creating visualizations, using Excel you were creating visualization, but Power BI helps you to create, uh, Power BI helps you to create something in a dashboard. There will be one empty canvas you will be given and you have to paste all those graphs over there just to present in front of the company. So that is what a Power BI is. Tableau is also the alternative of like— both are same tools, but whichever you find easy or whichever your company like where you wanted to get hired, so you can check it as per the requirement you can learn. Another is the data preparation and validation. So whenever you get the data, you don't have to directly go and apply stats like apply Excel tricks and apply Python. Everything that you are not going to do. First is the data collection. You have to think of the process how you're going to collect that data. Then you have to discover, like, you have to look at the data and look like whether this suits the problem or not. Then you have to clean that data. There might be something that you, you don't need it to solve that problem, so you can delete it. Even you can do the data transformation, validation, or publishing, something like that. You will conclude within the data preparation. After learning all these things, you have to do the data preparation. These are the steps for the beginners to start. This is not the step how data analyst works, so make sure this is something just a step-by-step guide so that you won't feel bored or you won't feel, you know, you can't do it. So you can definitely do it if you are going to follow this. Learn math, learn Excel, learn Python, learn SQL databases, and then learn about the Power BI. And data preparation and data validation. Then there is something analysis. So there are some features which we can analyze by just visualizing the data, by just looking at that data. So that is what you are going to do in the exploratory analysis, where you will be working on classification, regression, and clustering. So there are more concepts, but you don't have to learn everything, you know. Coding and programming, or I would say technical field, is something where you can't learn 100%. If you are learning Then learn, I, I would suggest 30% to 40%. That's it. That's the maximum what you can learn and you can get the job even. So I would suggest that in the exploratory also just cover these 3 basic topics and you will get the job. And once you have get the job, once you have the job, you can learn the things even. Even company is gonna pay for your learning, so you don't have to worry about that. Then the other thing is the machine learning. Scikit-learn, PyTorch. These three are the libraries, even only of Python, but just libraries you have to learn. Libraries means, uh, a package where everything is already given to you. Just have to— you have to just observe that why this function is used, for which purpose this function is used. That's it. After doing everything, once you are like— let's take a real world example that you are given a data, you have you know, done the data preparation, data validation, you have created graphs, you have created Python coding script also. At first you have taken that data on the Excel also, and you have implemented every maths and statistics, and you have decided that, okay, now I'm ready with everything, every decision that I could make out of this data. So what I— what you have to do is you have to do the data storytelling in front of everyone in the company. So your skill, communication skill, is the one of the best thing which recruiter will be looking at. So I would suggest that try to speak more, try to, you know, find out your mistakes only, like while speaking what mistakes you are making. So that mistakes will help you learn this storytelling. It's not just like you have to give the speech in front of everyone. No, you just have to tell to your company whatever you have observed from the data, that this is the data, I would suggest you that this time you shouldn't invest on this you should go with something else. Something like that also you can say. You don't have to every time say that yes, you can invest. No, the company is looking for yes or no. Company will ask you— if you are saying yes, company will ask you why. If you are saying no, it will ask you why. So storytelling is the one of the major thing that you should focus on. Once you have completed everything which I have provided on the screen, you are good to go. So we'll meet in the next video with the next concept.