If you're aiming to work in Data Science, here are five projects that will genuinely prepare you for the job market in 2026. And no- I'm not talking about the Titanic survival model. I'm not talking about the Iris dataset. And I'm definitely not talking about MNIST digit classification. Those projects teach syntax. But they don't teach how data science actually works inside companies today. I've been in this field for over ten years. I've reviewed hundreds of portfolios. I've hired data scientists. And I can tell you- the projects that land jobs in 2026 look nothing like the tutorials you see online. What hiring managers want now are end-to-end, business-aligned projects. Projects that show you understand the problem, the data, the tradeoffs, and the impact. In this video, I walk you through all five projects with specific examples, datasets, and resources you can use to start building right now. The 5 Projects: Customer Segmentation & Retention Analysis Demand Forecasting / Time Series Modeling NLP-Based Insights from Unstructured Data Experimentation & Uplift Modeling End-to-End ML System with Deployment You don't need to do all five. If you build even three of these well- with clean storytelling, sensible metrics, and thoughtful business framing- you're already ahead of most applicants. 💬 Drop a comment: Which project are you going to start with? Chapters: 00:00 – Why Classic Projects Don't Work Anymore 01:00 – Who I Am & Why This Matters 01:25 – What Hiring Managers Actually Want in 2026 02:26 – Project 1: Customer Segmentation & Retention Analysis 04:28 – Project 2: Demand Forecasting / Time Series Modeling 07:03 – Project 3: NLP-Based Insights from Unstructured Data 09:26 – Project 4: Experimentation & Uplift Modeling 11:47 – Project 5: End-to-End ML System with Deployment 13:40 – Quick Recap & Final Advice 13:54 – Free Resources & Outro Free Resources Mentioned: Datasets Kaggle Telecom Churn Dataset: https://www.kaggle.com/datasets/blastchar/telco-customer-churn Kaggle Online Retail Dataset: https://www.kaggle.com/datasets/vijayuv/onlineretail M5 Forecasting (Walmart Sales): https://www.kaggle.com/competitions/m5-forecasting-accuracy UCI Energy Consumption: https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption Amazon Product Reviews: https://www.kaggle.com/datasets/snap/amazon-fine-food-reviews Yelp Open Dataset: https://www.yelp.com/dataset Hugging Face Datasets: https://huggingface.co/datasets Tools & Libraries Prophet (Meta's Forecasting Library): https://facebook.github.io/prophet/ MLflow (Experiment Tracking): https://mlflow.org/ Weights & Biases: https://wandb.ai/ Streamlit (Data Apps): https://streamlit.io/ Gradio (ML Demos): https://gradio.app/ FastAPI (Prediction APIs): https://fastapi.tiangolo.com/ Sentence Transformers: https://www.sbert.net/ Recommended Reading Netflix Tech Blog (Experimentation): https://netflixtechblog.com/ Uber Engineering Blog: https://www.uber.com/blog/engineering/ 🔔 Subscribe for more AI/ML career tips, free resources, deep-dive educational explainers, and my personal journey navigating life in the US as an immigrant while building a career as an AI leader.
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You literally copied Dr. Fatih Hattatoglu's entire Medium article. Did you even say a word of your own at all?🤐
Could you please suggest a few AI projects for security professionals to start with?
Do you have a video for data science projects for beginners?
As a fresher which all data science projects should i add?? For working in data science and AI? Iam btech cse graduate.. should we add phone number in cv or not. Since iam planning mtech i can apply for remotely only
thank you so much, from ethiopia
this is already a 5 months worth work to showcase one can do these. or atleast 2.5 months, if one is doing it for long time.
Thanks, you are creating such a great quality content! Hope you come with videos of structured and production grade end to end project mentioned here!
You can try mixing music during slideshows, but when your face appears along with sound, music shall take background seat. I get what you are trying to achieve with music, just add a near-far effect in music. This is just a recommendation.
Where are mentioned resources link? 13:55
you have mentioned three problems for nlp project . which problem statement should actually suits for fresher as data science career
can you say how to do the NLP based project workflow
You are Spot on regarding the shift away from Jupyter Notebooks. In 2026, the barrier to entry isn't building a model; it's deploying it. A 95% accurate model in a notebook is useless if it can't handle real-time inference or drifts after a week. I'm seeing a huge demand for End-to-End Pipelines (using tools like FastAPI or Airflow) rather than just static analysis. We need to focus on building systems that solve the 'Business Survival' problem—handling messy, unstructured data and turning it into actionable insights, not just high accuracy scores. Agents and RAG are powerful, but only if they are wrapped in a robust architecture that ensures data quality before the LLM even sees it. Great video
I am so grateful for you Aishwarya , i had data science background but took 2 year break due to personal problem , trying to re-enter the industry , this video gave me a solid idea where i need to be giving my attention and energy on . Simple yet to the point video . Thankyou!!
didi aapne to dhak dhak kra dii mera to panda hi khatam hua tha aapki video dekh kr kahi m na khatam ho jau🫡
Very much informative video......I really like it. Your articulation and explainability is amazing. I will start project 2 first
How long would each of these projects take?
can we use AI, to get these projects done quickly? can anyone suggest which AIs Should i use to perform these tasks
These projects can definitely build a strong foundation for aspiring data scientists. At Lifewood, what we often see in real-world work is that the success of these end-to-end projects hinges on data quality and sourcing just as much as modeling and deployment. Clean, well-governed, and properly annotated data makes customer segmentation, forecasting, NLP, and experimentation far more meaningful in business contexts. Not every dataset will be perfect, but investing early in data preparation and validation is what turns these projects into skills that truly translate on the job.
Well done ma’am this is really helpful and well detailed for someone who is totally stuck.
Sound mix is bothersome here- background music too loud and speaking is too low.