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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Finally youtube algorithm did something good for me :)
EveryBody talking about project but no body talk about the depth of creation. Like - Industry standard, normal Level or just a project 😪 any body ?
One suggestion, music is not a plus
Background music is annoying, a headache.
Hi Aishwarya, I'm a self learning DS apprentice from Kerala. I found huge value from your content and grateful for investing your time and knowledge to help us.
Great job, finally someone explaining what data scientists actually do
Very helpful .... Please Keep more like these coming. If possible can you upload more detailed project videos discussing business value and architecture by picking up research blogs?
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.
Hey, I loved how you explained it so well. Could you please make a video on how to start and the important topics in data science? I’m having trouble connecting the dots, especially when it comes to mathematical equations. Seeking further help from you.
I have given 10 interviews at onsite for AI/ML role and found those are quite easy compared to indian interview for same AI/ML
lot of value is shared in this video, thanks for sharing!!
I wish finding the data wasn't such a nightmare. Helpful video though, thank you.
thanks for listening my request for DS project but also make roadmap for same
The background sound is too disturbing to focus on the actual content.
mam i am thinking of this project to showcase all the skills: 1.EDA[to show data anly. skill] 2. dashboard and kpi in power bi 3. ML 4.A/B testing 5.DL[in NLP i dont have interest in CV... is it a good decision to not to take CV] 6. GEN AI or LLM related [I will see ]
Tons of data again for free. Thanks again.
Thank you for the hints to excel as Data Scientist
Love the way you have done the videos, entertaining and informative
Really appreciate the depth of the video and reality of expectations. It was not just a 5 min video stating some cool projects with a click bait thumbnail. Thanks
Amazing work, appreciate the effort and attention to detail. Script was perfect and to the point, all of it good knowledge with no slacking or beating around the bush. Actually delivered what I was looking for, good editing, time well spent. Looking forward to more content!