How to Become a Data Analyst in 2024 | Complete Roadmap For beginners | skills required | DA Role

Ladies and gentlemen, hello everyone, welcome back to my YouTube channel. In today's video, I am going to share a roadmap for data analyst. Those who want to be a data analyst, don't forget to watch the complete video as I am going to share complete from scratch roadmap to learn the skills that are required for data analyst. So let's get back on the screen and see what are the skills required and how to learn those skills and what are the concepts that you need to cover in that particular. So let's get back. So roadmap for data analyst. So data analyst is a role where you have to analyze the data. The data will be given to you and you have to analyze the data using the skills, different skills. So let's see what are those skills. So the very first thing what you have to do is you have to learn about the introduction, then you have to go on to the Excel part, then SQL is required, programming skills is required, data collection, data cleaning, data analysis techniques, data visualization tools, projects, and portfolio. These are all 10 things that you need to do. You have to start from introduction and then you have to create a complete portfolio where you are going to show your skills to the recruiter that these are the projects that you have worked on, these are the skills that you already have. So let's see what are the in-depth things that you need to cover in these. So very first thing is the introduction. So in the, in the part of the introduction, what I mean by the introduction is that you have to understand what data analysis is, what is the role of the data analyst, what is the importance of like data-driven decision making. So basically data analysts are those who are given the data and on the basis of the data they have to analyze the data and make the decision whether the investor or the company's owner should invest in that particular thing or not, or how they have to deal with the customer, what is the customer requirement, and anything could be. So that is a part of the data analyst. So you have to learn about the role, that what is the role that you are going to offer if you are— and you are hired for the data analyst, and what are the skills required. You have to research about it. You can use LinkedIn for this purpose, or you can use Medium website for this. Another thing I would suggest you to do is go and ask ChatGPT what are the roles, all the questions that you have. Another thing is you have to also explore the industry use cases, that how the data analysts are being driven. Like basically, if the company is hiring you, then what purpose they are hiring, what your interest is in. If you are in finance, then case studies of finance. If you are in marketing, then read the case studies of marketing, and so on. So that's how you have to, you know, get a complete introduction about data analyst roles, importance, and how you are going to implement it, what will be asked to you, and everything. Then you have to continue your journey with the Excel. Excel means MS Excel, Microsoft Excel. You have to learn, you have to learn about the basic Excel functions like sum, average, maximum, minimum, so that you can analyze the data. In like, in short, you have to learn about the data manipulation, how to manipulate the data using Excel formulas and functions. Even you can do some basic visualization also on Excel. So you have to learn about some basic charting techniques also on Excel. Once you are done with the Excel, you have to proceed with the SQL. SQL is basically a database management tool which says that if you have a data, you wanted to back up the data, as the data is generated every single hour or every single minute, then how you are going to save that data, how you are going to work on that particular data which is generated every second or every minute, how you are going to work on it. So you have to learn about the basic queries. It is the database, so you don't have to learn about any programming in this. This is just a query base that you have to select this. There are multiple queries that you will be working on, like to insert a data, to update a data, to delete data. All these things you have to learn in this SQL data manipulation and join and aggregation as well. Once you are done with the SQL, you know about data, how to deal with the data, you know about how to do the basic manipulation and data visualization on Excel. So you have gone— gained the basic knowledge of these things. Then you have to go on to the programming language. So in the programming skills, you have two options. One is Python, another one is R. You can choose any one of them. I would say that Python is the best one to go ahead in the data analyst field because you can easily, after learning the data analyst, once you are at the good package at the data analyst, you can switch your job to the data scientist as well after learning few more skills. So I would suggest that go with the Python language if you wanted to upgrade your feature. So you have to learn about Python. Then in the Python, there are multiple libraries. Out of those, you have to learn about Pandas and the NumPy library. These libraries are good for data manipulation and analysis. And you have to also learn some libraries that are mainly for the data visualization, just like Matplotlib and Seaborn, which helps you create some visualizations. After you are done with the programming skills, you have grasped the knowledge of programming Python, and then you have to move on to the data collection part. In the data collection, the data is like— so everywhere is the data. So in YouTube also, on Google also, onto some websites as well. So what you can do is you can learn some techniques through which you you can collect the data. So very first thing is the web scrapping. It means you have to extract the data from the websites. There are multiple tools as well, multiple libraries are there which are available in Python. So you can use the tools like Beautiful Soup, libraries like Scrapy. All these tools will help you to do the web scrap. Another is the API-based. Some websites do not provide the direct access, or you cannot directly access the data. So in that case, they provide you the APIs from where you can understand how to collect the data from API. There are again multiple libraries of Python which you can use. One of them is the request library, which helps you to use the APIs to interact with the website and to collect the data. There are direct import methods as well. Suppose you have the CSV file, you have Excel file, so you can import those data into the Python environment for analysis. So that is what data collection means. So you have to learn about each and every technique of data collection. Once you have the data in your Python notebook, you have to continue with the data cleaning. Data cleaning means you have to handle the data. Data, uh, like there are different types of data. Even you have the data, suppose there are lacks of rows, but in those lacks of rows there are some missing values. How to deal with that? What are you going to input in, in place of that missing values? Or there are outliers, there are, there are multiple things that you need to do in the part of the data cleaning. So I would suggest you that you have to learn about how to deal with the missing value. Once you know how to deal with the missing values, you can go ahead with the outliers, which is to identify and handling like outliers. Like, um, outlier is something which is totally out of the range. There is some data which is out of the range, so you have to deal with those type of data as well. You have to learn about how to transform the data. Suppose scaling the data, there are categorical data, some data is in the text format, some data is in the numeric format. So you have to learn how to transform that data from one type to another type and how to do all these things within the Python. After that, you have to go on to the data analysis techniques. There are multiple data analysis techniques. First is the descriptive statistics, then you have to learn about the EDA, then you have to learn about the hypothesis testing, and even more type of statistics techniques are there. So you have to learn about all those techniques which will help you analyze the data better. So in the data analysis techniques, you have to learn all these things. Moving on to the data visualization tool, till now you have learned about few libraries of Python which help you for data visualization, just like Matplotlib and Seaborn. But there are tools that are required in market, so I would suggest that you can choose any one of them. You can either learn the Tableau or the Power BI. Both are good for data visualization. Basically what you will be doing in the Power BI and Tableau is you will be creating a dashboard dashboard which will showcase the entire data in one go to the company's owner or to your team. So that is the best thing, and that is why it is required in the market as well. So you can learn the tool, familiarize— like, you have to learn the interface, that how these tools works and what are the things that you can do onto the tools. You have to learn about creating bar charts, line chart, scatter chart, even the storylines are also there. So you have to learn all those Once you are done with data visualization, data analysis techniques, data cleaning techniques, data collection, and so on that we have discussed so far in this video, you have to now create projects which are the real-world project. You have to work on the real data sets. What you can do is you can use the Kaggle website, or even there are some government-authorized websites also. You can use those websites to get the data, and you have to work on that data. Once you have worked on that data, I would recommend you to post those things on LinkedIn so that you will have a good connection and a good network on LinkedIn to get a job. So you have to focus on the project which is relevant to the industry you are interested in. It's not like you are doing one project on healthcare, another project on finance, and so on. No, you have to choose your domain that in which you are interested. And suppose I'm a person who is more interested in finance, so I will do all the projects onto the finance so that whichever company is looking, which is whichever company is working in the finance domain, can hire me easily because they have, uh, you know, the trust, reliability that, okay, I am into finance, I know all about finance, the terms which are used in finance and everything, so they can easily hire me, there won't be any conflicts at all. So I would suggest you to focus on the project which is relevant to your industry in which you are interested in. You can do the collaborative project, which means that open source project are there. Open source project doesn't mean that you have to change a single word or a single letter and that is a contribution. No, you have to do the major changes onto the open source project to be the one which is totally out of the crowd. Like, you have to stand out of the crowd so that you can get the good job and the good package. So do open source projects as well. Once you have created the projects, create a strong portfolio. Once you have 5 to 6 projects, go on to the GitHub. On GitHub also you can create a portfolio. There is a github.io website which will help you to create a portfolio. Even you can use a Python inbuilt library also to create a portfolio. Portfolio is not like you have to upload all the things on your Drive No, it's like there would be one picture, there will be the diff— uh, description of your project which you have created, a screenshot of that project, and so on. As much project you have worked on, you have so many projects, so you have to add all those things onto that portfolio so that whenever the recruiter asks you for the portfolio, you have to send that particular link to the recruiter. This way you can do. So it's like personal website or the blog, we can say, where you have to develop the personal website and showcase your projects, insights, and learning. It's not like a complete website, I would say it is just a portfolio. Like on GitHub repo also you can do the same. You have to upload all those things on GitHub, create a markdown file, and add all those details all over there. You have to create a document, case studies based on your project, highlighting the problem, approaches, and insight. There are few tips that I wanted to give you that, uh, you can build a portfolio of the project. You can develop projects such as predicting sales trends or the customer segmentation to showcase your skills. These are just a few basic examples that I am adding up. You can join the Kaggle competitions as well. This is also the good way to get interacted with the recruiters and also to learn from the community. There few online courses and certification that you can enroll on. So enroll in the courses, which for this you can utilize the platform like Coursera, edX, Udemy, DataCamp, or even Great Learning as well. So you can gain the learning and the validation as well through those courses. So certifications will be provided to you, so you can add those certification onto your LinkedIn profile. You have to stay informed that what is going on in the latest trend onto the market, like which are the tools required, what are the techniques used, and you have to read those blogs as well. So So just like you are active on Instagram, you are active on WhatsApp, stay active on LinkedIn as well. This will help you to grow in your future. You have to explore the advanced topics as well. Suppose there is a company who is— who has mentioned the data analyst with the machine learning basics. So you have to learn about basics as well, that what machine learning is, which are— what are the things that have— we have to do in the machine learning, what exactly machine learning is dealing with. So you have to grow day by day. So till once you have done with the projects and the portfolio of the data analyst, you can start applying for the jobs. So I think this is enough for the data analyst. If you want to be a data analyst, don't forget to follow the entire roadmap. In case you need any help, you can DM me on my Instagram or drop your comments in the comment section. I will surely read them and direct you to the resources or whatever help I can provide you with. So thank you everyone for watching this video.