#Data Science Project

60 Unique Data Science Project Ideas Also see 👇 Top Places to Grab Public Datasets Kaggle Datasets Google Dataset Search Hugging Face Datasets Hub UCI Machine-Learning Repository AWS Open Data Registry Microsoft Research Open Data BigQuery Public Datasets (Google Cloud) Data.gov (USA) data.gov.in (India) World Bank Open Data IMF Data Portal Eurostat Open Data FiveThirtyEight GitHub Awesome-Public-Datasets (GitHub) OpenML Zenodo Figshare PapersWithCode → Datasets Quandl Registry of Open Data on Azure Special Benefits for Our Instagram Subscribers 🔻 ➡️ Free Resume Reviews & ATS-Compatible Resume Template ➡️ Quick Responses and Support ➡️ Exclusive Q&A Sessions ➡️ Data Science Job Postings ➡️ Access to MIT + Stanford Notes ➡️ Full Data Science Masterclass PDFs ⭐️ All this for just Rs.45/month! . . . . . . . #datascience #machinelearning #python #ai #dataanalytics #artificialintelligence #deeplearning #bigdata #agenticai #aiagents #statistics #dataanalysis #datavisualization #analytics #datascientist #neuralnetworks #100daysofcode #genai #llms #datasciencebootcamp #dataengineer

The best projects serve a real use case Comment “data” for all the links and project descriptions #tech #data #datascience #ml #explore

If you want to crack Data Science jobs in the next 30 days, here’s the three step process which you will follow which literally no one talks about. . . . #datascience #data #interview

Comment ‘Projects’ to get 5 Data Scientist Project ideas and a plan 👩🏻‍💻 ♻️ repost to share with friends. Here is how to become a data scientist in 2026 and beyond 📈 the original video was 4 min Andi had to cut it down to 3 because instagram. Should I do a part 3v what are other skills that you would add to the list and let me know what I should cover in the next video 👩🏻‍💻 #datascientist #datascience #python #machinelearning #sql #ai

Comment PROJECT to access my step-by-step Python tutorial that anyone can follow to build your very first geospatial dashboard web app! 🌍📊 A good number of portfolio projects is 3–5, and the types of projects you choose should reflect the kind of data role you’re going after. A data analyst portfolio should look very different from a machine learning engineer one. Even within data science, a product/decision data scientist portfolio should focus on A/B testing and metrics storytelling—while an algorithm data scientist portfolio might highlight modeling and experimentation. ✨ Especially if you’re building your very first project, prioritize: 🌱 Real-world messiness (not polished Kaggle sets) 🌱 Business context and decision-making 🌱 Clear documentation (what you did and why) 🌱Visuals to help your work stand out No one’s asking for perfection—they want to see how you think. #datascienceportfolio #dataanalyst #learnpython #codingjourney #techcareers

Comment “project” for my full video that breaks each of these projects down in detail with examples from my own work. If you’re using the Titanic, Iris, or COVID-19 dataset for data analytics projects, STOP NOW! These are so boring and over used and scream “newbie”. You can find way more interesting datasets for FREE on public data sites and you can even make your own using ChatGPT or Claude! Here are the 3 types of projects you need: ↳Exploratory Data Analysis (EDA): Exploring a dataset to uncover insights through descriptive statistics (averages, ranges, distributions) and data visualization, including analyzing relationships between variables ↳Full Stack Data Analytics Project: An end-to-end project that covers the entire data pipeline: wrangling data from a database, cleaning and transforming it. It demonstrates proficiency across multiple tools, not just one. ↳Funnel Analysis: Tracking users or items move from point A to point B, and how many make it through each step in between. This demonstrates a deeper level of business thinking by analyzing the process from beginning to end and providing actionable recommendations to improve it Save this video for later + send to a data friend!

Here’s a roadmap to help you go from a software engineer to a data scientist 👩‍💻 👇 If you’re tired of writing vanilla apps and want to build ML systems instead, this one’s for you. Step 1 – Learn Python and SQL (not Java, C++, or JavaScript). → Focus on pandas, numpy, scikit-learn, matplotlib → For SQL: use LeetCode or StrataScratch to practice real-world queries → Don’t just write code—learn to think in data Step 2 – Build your foundation in statistics + math. → Start with Practical Statistics for Data Scientists → Learn: probability, hypothesis testing, confidence intervals, distributions → Brush up on linear algebra (vectors, dot products) and calculus (gradients, chain rule) Step 3 – Learn ML the right way. → Do Andrew Ng’s ML course (Deeplearning.ai) → Master the full pipeline: cleaning → feature engineering → modeling → evaluation → Read Elements of Statistical Learning or Sutton & Barto if you want to go deeper Step 4 – Build 2–3 real, messy projects. → Don’t follow toy tutorials → Use APIs or scrape data, build full pipelines, and deploy using Streamlit or Gradio → Upload everything to GitHub with a clear README Step 5 – Become a storyteller with data. → Read Storytelling with Data by Cole Knaflic → Learn to explain your findings to non-technical teams → Practice communicating precision/recall/F1 in simple language Step 6 – Stay current. Never stop learning. → Follow PapersWithCode (it's now sun-setted, use huggingface.co/papers/trending, ArXiv Sanity, and follow ML practitioners on LinkedIn → Join communities, follow researchers, and keep shipping new experiments ------- Save this for later. Tag a friend who’s trying to make the switch. [software engineer to data scientist, ML career roadmap, python for data science, SQL for ML, statistics for ML, data science career guide, ML project ideas, data storytelling, becoming a data scientist, ML learning path 2025]

5 Data Science Projects That will actually get you Hired, With Tutorials Building a Three-Model, Real-Time Fraud Detection System with FastAPI Predictive Maintenance using Machine Learning Customer Segmentation Using Machine Learning (K-Means Clustering) – Full Python Data Science Project Fraud Detection Using Machine Learning – Full Python Data Science Project (94% Accuracy) Data Science & Machine Learning Projects by CodeBasics Channel More details in bio {kushal vijay, careerguidance, data science projects, viral, trending, project ideas, templates, microsoft, coding projects, data science jobs, ml projects} #codingprojects #careerguidance #viral #trending #kushalvijay #career

DATA SCIENCE ROADMAP FROM GOOGLE DATA SCIENTISTS . . . #datascience #google #nodaysoff #AI #sql #python #roadmap #cheatsheet

Don't miss these👇🏻👇🏻 150+ Data Science projects you can try with Python 🖇️ Link👇🏻👇🏻 https://python.plainenglish.io/85-data-science-projects-c03c8750599e Comment “Projects” for the link and send the reel in my dm to get early access Don't forget to share the reel with your fellow friends 😉 Project research credit goes to @amankharwal.official i have just found these projects on internet 😀 Hashtags 🔎 #DataScienceCommunity #DataScience #DataScienceProjects #DataScienceEducation #datasciences #Python #PythonProjects #PythonProjects

🚨 Most Data Portfolios Are Just… Noise! 🚨 ❌ Random charts for the sake of charts ❌ No real context ❌ Zero business value So I fixed that. 💡 📦 I put together something game-changing: ✅ 80+ Real Project Ideas ✅ Sorted by Industry (E-commerce, Finance, Healthcare, AI, Web3 & more) ✅ The kind of work that actually gets you HIRED 💼 🔥 This is your roadmap if you’ve been stuck wondering “What project should I build?” 💬 Want access? Feel free to DM me “Project” and I’ll send you the details! 📌 Save this so you don’t lose it 💬 Comment your dream industry 📤 Share with a friend stuck in “what to build” mode ❤️ Like if this inspired you 🔖 Hashtags: #dataanalytics #portfolioprojects #datascience #sql #powerbi #dataanalyst #analyticscareer #dataportfolio #machinelearningprojects #aiprojects #dashboarddesign #careerindata #instagrampost #trendings #trendingreels❤️ #fvpシ❤️💞❤️

3 data science projects you can do in a weekend. If you’re learning data science, one of the best ways to improve is by working through real examples. Here are three Kaggle notebooks you can explore: • Loan Prediction – predicting whether a loan gets approved based on applicant data. • Bank Churn – analyzing which customers are likely to leave a bank. • House Price Prediction – estimating house prices from property features. You can study the notebooks and also try solving the same problems yourself using the same datasets. It’s a great way to practice and see different ways people approach the same problem. Comment “DATA” and I’ll send you the notebooks. #coding #datascience #university