Stand Out! Create a Unique Data Analyst Project (Step-by-Step Guide)

Ladies and gentlemen, hello everyone, welcome back to my YouTube channel. In today's video, I am going to tell you about how you can create a data analyst unique project. So there are multiple ways to do that, but now I am going to tell you how you can do it on your own. If you wanted to create unique projects for your resume and you're using me, not getting shortlisted, then you can try this method to create unique projects and get select for your interviews. So let's get back on the screen and see what are the steps, what are the things that you need to do for data analyst role in any company. So let's first see what data analyst role is for. So in the data analyst role, you will be, uh, tasked to, you know, perform, uh, multiple things onto the data itself. So you will be tasked for transforming the raw data into the actionable insights that can drive our decisions for the business. So, uh, in short, if I will say that this is what you have to do, but let's see in depth what are the steps that you are going to follow to convert your raw data into the decision of that for the business. So in the data wrangling, you will be doing data acquisition, which means that you will be identifying, you will be extracting, you will be connecting the data. Once you have collected the data, you have to perform data cleaning, then you have to perform the data transformation. Following up with the data analysis, you have to perform EDA, which is exploratory data analysis. You have to learn about the statistical analysis, data modeling. If I talk about the data communication, so for communicating what you have learned or what you have gained through the data, you have to create visualization. It means you have to create graphs, charts, dashboards so that you can tell your non-tech team about the insights that you have extracted from the data. You have to create the reports, you have to be ready with the storytelling. And if I talk about the technical skills, then you must learn some, like one programming language and the data analysis tool as well. If I talk about the programming languages, then you have to learn about the SQL, which is Structured Query Language. Second is the Python or R. You can choose either the Python programming language or the R programming language. Both are the similar, but there is a few difference also. So yes, Python is a versatile one. You can go into the machine learning field also with the Python. So I would recommend you to go with the Python only. And if I talk about the data analyst or manipulation tool, then you have to learn about the Microsoft Excel and Google Sheet. If I talk about data visualization tool, then you have to learn about the Tableau and the Power BI. There are few libraries also of Python that you need to learn. One of them is the Matplotlib, another one is the Seaborn. You can also create dashboards using the Python library itself. So the, the name of that library is the Plotly library. So you have to explore upload the Plotly library as well. So apart from that, you have to learn about the version control system. Version control systems are those that helps you, uh, to be— to track your changes in the codes and allow the data files to like from the local to like basically to hosting it. So that is the version control system. You have to learn about Git and GitHub. This will help you to control all these things. These are the specific tools that are required for data analyst role. If you wanted to be one, you have to learn all these skills. If you have gained all these skills, then now let's see how you can create data, where you can get the data, how you can create beautiful and a good project for your resume, and how you can be unique and stand out to the crowd. Now let's see how to create a data analyst project if you have already these skills. So to create a project, first what you need is the data analyst project idea. So project idea needs to be unique. If you will check out the LinkedIn, you will check out Kaggle, you will found that there are the same projects uploaded. Like the people who are into— who are coming into the field of data science are creating the repeatedly same project. If I talk about credit card fraud, then they have all have created the credit card fraud project. But I would recommend you to create something unique if you wanted to stand out of the crowd. So for doing that, I will tell you what you can do. So first thing is you need to tell— talk about your interest, that which type of a projects excites you most. Like if I talk about like I am into the business, then I love talking about business things. I know about the terms that are used in the business. Then I will create something within the business domain. So this will help me grow faster. So if you ask me that you wanted to get job in 3 months, 2 months, then you have to be in your domain. If you are switching your career from one domain to another, then you have to be in that domain in which you were in. If you are into the sales, then in which domain sales you were working on. You were working on the retail side, or you were working for the business like e-commerce one, or what? So if you are somewhere in the sales, then create something related to sales. Now I'm going to tell you about some domains on which you can be. You have to think about your interest and what kind of data analysis you find exciting. So if you are into business, then you can create business analytics projects like general business problem you can take, or you can take something related to finance or marketing. If you are into the marketing analytics, then you have to— you can create something like customer behavior, advertising campaign, or marketing research. You can get these type of data and create projects like this. If you are into the finance, you were into the finance and now you are switching into the tech field, then you can learn about the final— like you can create projects about the financial market, risk management, fraud detection, healthcare, then patient data, clinical trials, and public one. If you are into the retail side, then customer behavior, inventory management, how you did the pricing strategies, how should business do the pricing strategies and all. You should create projects like this. If you are an HR team and you wanted to switch now into the data field, then you should create something related to the recruitment process or employee performance or retention rates and all. If you are into the sports one, then you can create something players performance. You might have seen that there are multiple ads coming up of ClickBet or some casino and all, right? So you can create something related to that also if you are more into the sports size, because there are some— there are many companies who are hiring for the sales data analytics. Social media, if you are social media manager, if you have worked on social media, you have like multiplied your followers and all, then you can create something related to the social media. If that is something that excites you the most, then you can create. Brand sentiment, social media trends, how audience insights has to be, you know, taken in the hand and all. Manufacturing analytics, then you can go with the e-commerce, then you can go with the media and entertainment. There are one domain like travel and hospitality, education, nonprofit, scientific research, security and fraud analytics, and legal analytics. So these are few domains. If you are in any one of them, the— these domains, then you can go explore these domains and talk about these domains, what are the terms used in this domain, and then and build the project in that particular. Now another things comes up. Suppose you are taking anything like social media, then where are you going to get the data? There is only one site? No, there are multiple sites to get the data. If you want any of the data, you can explore these free sites. One of the evergreen site we all know, that is the Kaggle website, which gives you the variety of datasets, whether it is manufacturing, whether it is quality control, production, or anything. So you just have to go on to this website, you can get the data easily. Another website that we have is the UCI Machine Learning Repo. UCI Machine Learning Repo also gives you variety of data and you can explore much more on this website. Another is the US Census Bureau manufacturing data, and on this you can analyze the data of an establishment or shipment, something related to that. There is also one more website which is called FRED, which is Federal Reverse Reserve Economics data, and this also gives you— you can search by the category. I have added these descriptions on the basis of the manufacturing one because people used to ask that we are from manufacturing, how we can get the data? I'm not finding any data relatable to that. I am getting onto the Kaggle and I'm getting education data and I'm getting the finance data, but I'm not getting the manufacturing one. So that is why I have added that how you can go on to the manufacturing and the production data. If I talk about the World Bank one, this is also one of the good website to get the data about manufacturing value. All I told you about manufacturing I have added, but you can search about the more data type data sets as well. But if you are a beginner, I would suggest you to go with the beginner-friendly datasets. This will help you get the confidence on what you are creating and what you have created, and this will also enhance your confidence when you are going to present your project in the interview. Once you have selected the data and you have also gone through the data that you have already found the data into the domain, into the, uh, the domain you were interested in, then the next step is to define the project goal, that which type of a project you are going to create. This will basically— you have to try to answer the question what your analysis is gonna be. So let's take an example. Suppose you are going with a retail sales analysis. So you, let's say you have the retail store which is analyzing, like, is analyzing the sales data. The question might be that what are the top selling products by region and can we identify any trend that will help with the future stocking decisions? So that is how you have to frame a question related to the datasets that you have selected. If I talk about the social media marketing, then question could be which social media platform is generating most engagement and what type of a content performs best on that platform. So that's how you have to frame the questions related to the domain and related to the data that you have selected. If I talk about the website traffic analysis, then in that case the question would be, where is our website traffic coming from? Or are there any specific demographic where we should target more in our marketing efforts? So you have to look from the business perspective that what could be the question that I can solve from this data. If I talk about the customer satisfaction, then the question would be, what are our customer satisfaction level and how we can improve the customer's experience? Apart from that, if I talk about the scientific experiment, then the question would be, does this new drug have any significant side effect, or are the benefits worth the potential risk? So you have to think from the business perspective, then you have to frame a question, and then so that you can make your data cleaning, data visualization steps easier. If you know what you are targeting, you know about what you are looking for, you can easily filter out your data. If you know what you are targeting, then you can easily create the graphs on the basis of the question that you have framed. It's not like you have taken one data and you can solve only one problem. No, within the data you can solve multiple problems. So try to frame multiple questions. You can also use the AI tools for framing the question related to the data that you have. So once you have the data, you have to do the data acquisition. As I told you, you have to download the data from any of the website that I told you earlier. You can pause the video and you can go back and search or note down those 5 websites that I told you. And then you have to perform the data cleaning. You have to look for any missing values if there is any. Then you have to perform the EDA. In the EDA process, you have to summarize your data. You have to look for any pattern that your might— your data might consist of. And you have to identify the relationship between the variables, which is the column. Suppose there are multiple columns, like 10 to 12 columns. So you have to look for the relationship between each column. So like, which column is affecting the other column and how is it affecting? Then you have to perform the data analysis. In the data analysis, you have to go with the statistical methods, techniques, and to answer your projects. Make sure that whatever you are doing, this includes every— whichever project you are selecting, you have selected the datasets, make sure that you are trying to implement all the skills that you have learned whether using YouTube, whether using the courses, or you have enrolled somewhere. You have to accomplish all those things at one place so that you can present it in the better way. Then you have to create the data visualization and then following up with the documentation process. Documentation process is very important, I would say. Whenever you are documenting your project, you have to come up with the particular, you know, scenario that this is what you have to follow, this is— this should be the outline of your document. You have to come up with the datasets from where you have done the data acquisition. What was the most difficult part that you encountered while you were doing the data cleaning? What was the step that you have followed? What was the unique technique that you followed? And which type of a graph you have implemented and why you have implemented that graph. That is how you have to build your documentation so that whenever the interviewer is gonna ask you, you will be, you know, it will be in your hand and you can just have an eye onto the document and you will remember everything that this is what you did and this is why you did., and that would be easier for the interviewer as well and for you as well to explain the project. After doing that, you have to create at least 4 projects for your portfolio. If you have 4 unique projects that are not built by other, uh, applicants, then your resume is for sure gonna be selected for the interview. So for that, you have to create a portfolio in which you are going to add all 4 resume, all 4 projects. So that's how you can create a data analyst project. If you want me to create a video on how to present your projects in the, in the interview, then you can drop down your comments. I will surely create a video, complete video onto that. And also, if you are looking for the same of the data science, then I am— I will surely create one. You just have to drop the comments in the comment section. Until the next video, bye!