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/


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Five portfolio projects are recommended for aspiring data scientists: customer segmentation and churn prediction, time series demand forecasting, NLP-based insights extraction from unstructured data, AB testing and uplift modeling, and end-to-end ML system deployment. Each project emphasizes business alignment, real-world data handling, trade-off analysis, and practical impact over basic tutorial exercises. The focus is on demonstrating full lifecycle understanding from problem definition through deployment and monitoring.

5 Data Science Projects that will Get You into Big Techs | Ex-Google, Microsoft