In this instructional video, Akansha Yadav guides aspiring data scientists on how to design unique, impactful portfolio projects that differentiate them in the job market. She begins by outlining the full data‑science pipeline—from problem framing and data acquisition through cleaning, exploratory analysis, modeling, evaluation, deployment, and monitoring—emphasizing the often‑overlooked deployment stage. Yadav then lists the essential technical skill set (Python, Pandas, NumPy, scikit‑learn, TensorFlow, cloud platforms, visualization libraries, statistics, and communication). The core of the talk focuses on selecting a passion‑driven domain (e.g., NGOs, manufacturing, marketing, healthcare, logistics, media, finance, travel) and crafting a business‑centric project using a three‑part framework: clear business question, analysis plan, and quantified impact. She provides concrete example scenarios (e-commerce recommendation, retail inventory optimization, churn prediction, dynamic hotel pricing, equipment‑failure prediction) and recommends open‑source data sources such as Kaggle, UCI, US Census, FRED, and World Bank. Practical advice for beginners includes the 80/20 rule (most time spent on cleaning), choosing manageable datasets, and iterating through the pipeline. Yadav stresses the importance of storytelling, compelling visualizations (Plotly, Bokeh), stakeholder communication, and deploying models to the cloud. Finally, she suggests ways to stand out: using advanced techniques (time‑series, NLP, deep learning), contributing to open‑source projects, creating interactive dashboards, and quantifying real‑world impact (e.g., percentage sales lift). The video concludes with a call to action to like, subscribe, and check her resume‑building video.
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Ladies and gentlemen, hello everyone, welcome back to my YouTube channel. In today's video, we are going to talk about data scientist role, that how you can create unique projects for data scientist role. So everyone is creating the duplicated projects, credit fraud or something related to the time series, and it's, it's all copied project. But I want you guys that my audience should create something unique. I have already given you multiple resources on Instagram that how you can get the free datasets, or what are the websites that you can explore other Kaggle for datasets. But now in this video, we are going to talk about for data scientist role, what are the skills required and how you can, uh, create a unique project. This main goal of this video is how to create unique projects. So what is the data scientist for? So data scientists are the rock star of the data world, obviously, because they are the one who are going to take the complex datasets and have to make the predictions for the business. So in the data science pipeline, the first thing is the problem framing and the data acquisition. So first you have to do the— you have to collaborate with the stakeholders to understand the business problem and then translate it into the data-driven questions. You have to frame multiple questions for addressing the business problems, and then you have to identify and acquire the relevant data from the sources including database, APIs. You might have to use the web scrappings as well. And then you have to do the data wrangling. It is— it means you have to clean the data, you have to process it, you have to transform basically the raw data into the one which is ready for analysis and the visualization and for the predictions as well. Then you have to employ the statistical method and then data visualization techniques. You have to, uh, implement to the— so that you can cover— you can uncover the patterns that are in the data, and you have to get the insights from the data as well. Then you have to build the model for machine learning. It can be of any model— classification, regression, clustering— to solve the problem. Then you have to fine-tune the model as well so that your model performance is really good. You have to optimize your model performance by fine-tuning your model. Then you have to perform the evaluation and iteration onto the model. If you are ready for development, it— the main process that everybody, every beginners misses is the deployment. You have to learn how to deploy the models. That is a major thing that everyone is missing out. So you have to clearly communicate your complex insights and you have to monitor and maintain the model over the time to ensure the continued effectiveness. Effectiveness. Now let's see what are the technical skills required for this. So you have to learn about the programming, that might maybe the Python programming. You have to learn about the data libraries, that what are the libraries— Python, Pandas, NumPy, scikit-learn, TensorFlow. And then you have to learn— you should also learn about the statistics and the mathematics. And then you have to learn about the machine learning. Some libraries are there for machine learning which I mentioned above, which is scikit-learn, all the TensorFlow. You have to also learn about the cloud computing so that you can easily do the data storage, data training, and deployment onto the cloud itself. And then you have to also learn about the data visualization. You must have the excellent communication skills so that you can interact with the stakeholders, you can communicate with the non-tech team, you can communicate what you have understood or what your model has created, and so that you can come up with the best business need and the best business solution. Now let's see how you can craft a standout portfolio for data scientists. First is you have to think of a passion that what are you interested in? Don't just follow the crowd. You have to explore topics that you genuinely find interesting, whether it is sports, movie recommendation, or climate change, or something related to NGOs, or anything it could be. So you have to find your interest. That suppose I am the one who is interested in finance, I am the one who is interested in marketing, then I will go for something within the market. I will create model for marketing. If I am a social media manager, I will go for the social media manager prediction projects. So that's how you have to be the industry relevant. You have to be specific while you are creating the project. There are some different domain ideas. Suppose you are interested in the nonprofit organization, so data scientists can use their skills at NGO to analyze the data on the social issues like poverty, hunger, climate change, or anything. And you might have to also build models to predict the area of greatest need, or maybe something like resource allocation. And there are multiple things that needs to be done in the NGO, and that is the role of the data scientist. As well. If I talk about the manufacturing, if you are the one who is interested in manufacturing, then you can opt for the manufacturing process, uh, projects. And you have to analyze the sensors data from machine to predict the equipment failures or improve the protection for that, or you have to increase the efficiency of that product. Then marketing, one also you can explore. Healthcare providers also use data scientists to identify the potential health risks, personalized treatment plans, develop new drugs, and many more. Logistics and supply chains, like analyzing the data on transportation routes, inventory levels, customer demand, and they also have to leave something to reduce the shipping cost and also improve the delivery time. That is the major thing in the logistic and the supply chain management. If you are someone from the media and the entertainment one, just like I gave you an example of the social media manager or something, then you have to also personalize the content recommendation for the user, analyze the audience performance preferences, and also Optimize advertising campaigns. Then after you have already selected that this is my domain and I am interested into this, you have to get the data. Once you have the data, so basically suppose you have selected some— let's take an example of social media. You have selected that I am more interested into social media. So once you have selected social media, you have to think of the business problem that others might be facing, maybe related to the content or maybe something else. You have to think and you have to frame a question for this. So first is you have to focus on the question that has a clear business implication and how you can analyze, improve efficiency, boost sales, or something like that. So let's take an example of the e-commerce company. Can we personalize product recommendation to increase customer conversion rates or order value? So what you can do is you have to think of 3 things. First is your question, then you have to think about the analysis that you are going to do, and then you have to do the impact. Then you have to think about the impact, that what would be the impact. So this is what we are looking forward for analysis: user behavior data, I need purchase history, product attributes to build the recommendation engine that suggests the relevant product to each customer. So analysis means— doesn't mean that you have to think about data cleaning or data modeling, fine-tuning. No, that is a secondary part. First is you have to think from the business perspective that how you can help business and what is the real problem that businesses are facing and how you can, you know, propose the solutions to them. Once you have proposed the solution, you also have to think about the impact because you have to show them the realistic case that, okay, this is what is happening and this is what we are going to do and this would be be the impact. That's how you have to take it forward. So same way, I have added an example of the retail supply chain. Suppose the question would be, how can we optimize inventory management to minimize the stockouts and overstock? That is a major problem that retail stores faces. And then you have to think about what is the exact requirement of yours. You have to analyze the historical sales data, seasonality trends, and customer demographic to predict the future demand for each product at each store. So that is how you have to pretend, and that is how you have to propose in front of the stakeholders while you are communicating or collabing with them. Then you have to do the impact. Then you have to think about the impact and you have to convey the same to the stakeholders that this would be the impact if you are going to do this. If I would have this data, then I will— then I am going to make this, this impact on your business. Same way for the financial services, the question would be, can we identify customer at a risk of the churn and develop targeted marketing campaign to retain them? So let's first see what churn is. Churn is basically a percentage of a customer who loses the business's subscription or something similar to that. This is mainly— this mainly happens in the subscription-based companies who provide you annually, monthly, or the yearly subscriptions. And there are some customers that who are— who were taking this subscription from last 2 to 3 years, but this year they cancel the subscription. So what could be the reason? How we can retain them? And that is the negative impact on the— on the businesses. So businesses looks forward for the data scientists to help them to retain the customer. So they— the— what you need is you need the customer account data transaction history, demographic, to build a model to predict customer churn. And you will tell the impact as well, that this will reduce the customer churn, this will increase the customer lifetime value, this will be more effective if you are going to make the marketing campaign, then this is gonna be the plus point for you. If you are going to, if you are going to provide me this data, this is what I could do for you. In case of the travel and hospitality industry also, we need a data scientist. If the question would be then, how we can dynamically adjust hotel room pricing based on the real-time data and the competitive price. So for this, what do I need is I need historical booking data, seasonal trends, competitor pricing, local events to predict optimal prices for the hotels. And this would be the impact. Same way for the manufacturing company as well. So you can predict equipment failures before they occurs, allowing for the prevention, maintenance, and minimizing the downtimes. In case of the healthcare, there are multiple equipment that are used in the hospitals and they might raptures at any time and You might have to— for like, the doctors might have to face the downtimes as well, but this is the best way to prevent it. So you have to think about the analysis, that what type of a sensor data do you need for this analysis and what is the impact that you are going to make. Then you have to think about the data available. First, think from the business perspective. What are the questions? What are the business's problem that you can face? Then think of the data that you need for this project. So for this, you can Explore multiple open source datasets just like Kaggle, UCI, US Census, FRED, or World Bank open data. You have to download the datasets and then you have to take it forward to the Jupyter Notebook to create the models. If you are a beginner, make sure that you are choosing the beginner-friendly data. Don't take something which, which will, you know, just burst your mind just because the messy data is the one which is, you know, data scientists in the data science field, there is one rule which is 80/20 rule, which means 80% of your times you are going to spend on data cleaning and 20% you will be using for model. So the data cleaning is the major part. So make sure you are choosing the data which gives you the confidence. Once you have completed the project, you will get the confidence and you will be willing to create the next one. But if you are going to take something which is very vast and very messy data, then you are not going to end up with the complete project. You will feel fed up with, okay, I'm not interested to do it further. So go with something which is beginner friendly. Then deep down, deep down into the— dive down into the data science pipeline, which says that you have to do that data acquisition, cleaning, EDA, model iteration and everything. Then you have to communicate your findings like a pro to the, uh, non-tech or tech team or to the stakeholders. You have to do the storytelling. Don't just present the results, you have to create a compelling narrative that highlights the problem and your approach and the insights you uncovered earlier. When you were interacting with the stakeholders, you have— you were thinking from the business perspective, but now you have the data, now you have created the model, now you know the realistic results. Now you have to tell the problem, your approach, your insights, and how this is gonna impact the marketing campaign or the business. Then you have to create the data visualization so that you can easily convey the information to the team. And then you have to deploy the model and monitor. So for deploying and monitoring also, there are multiple things that you need to learn. So make sure you are learning the deployment as well before you are applying for any company. Tips to stand out is that you have to go beyond the basics. Don't settle for just the linear regression model. Think about the more advanced techniques like maybe time series analysis, natural language processing, or deep learning. It totally depends upon your interest. But yes, this This is gonna give you the exposure of the variety of problems. You have to work on the open source work. You can go for the data publicly available on the platforms like GitHub, or you can also use the Kaggle competition for this, or the another virtual internships options are also available to you. Then you have to also use the interactive elements like polyvisualization. You can use the Plotly or Bokeh. There, these are the libraries of Python and R, so you can use this for creating the compelling dashboard. So for data scientists or role, you don't need to learn about the Power BI or the Tableau. You can use the Python inbuilt libraries like Plotly or R library. Then you have to— real world impact. You have to quantify what you have gained from this. You have to quantify that this is the percentage that I increase their sales. For suppose you were working on the sales project of any company as an intern, so you have to mention it in your portfolio, mention it in your resume, that by 15% the businesses— the business of that company increases because of me. I was working on this particular project and this is what I did, and that's how I helped them grow by 15%. So you have to quantify your outcomes. So that's how you can craft a compelling and unique projects for your data science role. I hope this video helped you a lot. Don't forget to hit the like button and subscribe to my channel for more videos like this. I have also created how to create a resume video onto my another channel, which is in Hindi. So you can go check that out, and the link is in the icon. And in the next video, bye-bye!
