#Data Science Projects

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

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

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

Data analyst Projects Ideas 💡

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

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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!

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 "Projects" & I will DM You all the projects! Here are 5 Python projects that can actually help you get hired: 1️⃣ Medical Data Extraction 2️⃣ Sports Celebrity Image Classification 3️⃣ T20 World Cup Cricket Analytics 4️⃣ Grocery Store Management System 5️⃣ Sales Insights for a Consumer Goods Brand Like this reel, comment “Projects”, and share it with your friends! Must follow to receive the source code in your DMs 👇🔥 #pythonprojects #datascienceprojects #pythonforbeginners #jobready #techreels #pycodehubb

Here are 3 unique data science projects you can build in a weekend (2026 World Cup) easy. a World Cup match outcome predictor. predict win, loss, or draw using historical FIFA data. tech stack: Python, Pandas, Scikit-learn, and Streamlit to deploy it. medium. a player performance dashboard. pull player stats from Transfermarkt, visualize everything, and cluster players by playing style. tech stack: Python, Pandas, Plotly, and Seaborn for visualization with KMeans for clustering. hard. a real time World Cup sentiment tracker. pull live tweets during matches, run sentiment analysis as goals happen, and visualize how public opinion shifts in real time. tech stack: Python, Tweepy for the Twitter API, HuggingFace Transformers for sentiment analysis, and Plotly Dash for the live dashboard. comment “World cup” for resources to help you out along the way. #machinelearning #datascience #ai #python #cs

9 data projects ideas instead of doomscrolling: (And there is a repo with the data :) ) 🤖 Machine Learning Projects * Diabetes Classification
Build and compare classification models to show how data preprocessing, feature scaling, and hyperparameter tuning directly improve predictive performance. * Heart Attack Prediction
Implement an end-to-end classification pipeline—from raw data to model evaluation—to demonstrate a realistic machine learning workflow. * Medical Cost Prediction
Train a regression model to predict healthcare costs, emphasizing feature importance analysis and model optimization to explain what drives predictions. 🛠️ Data Engineering Projects * NBA Player Statistics ETL Pipeline
Design an ETL pipeline that extracts player statistics, cleans and transforms the data, and stores it in a relational database for reliable downstream analysis. * Real-Time & Batch Data Pipelines with Kafka
Build a scalable pipeline that processes streaming and batch data using Kafka, PostgreSQL, and Docker to demonstrate modern data flow architecture. * Glassdoor Job Data Pipeline
Scrape job postings, clean and structure the raw data, and prepare it for analysis and visualization to showcase real-world data ingestion challenges. 📊 Data Analytics Projects * Pokémon Dataset Analysis
Perform exploratory data analysis and feature engineering to uncover patterns in Pokémon characteristics such as types, stats, and legendary status. * Automated EDA Tool Comparison
Benchmark AutoViz, SweetViz, and Pandas Profiling across multiple datasets to evaluate performance, resource usage, and practical trade-offs. * Exploratory Job Market Analysis
Analyze cleaned job posting data to extract trends, key skills, and role distributions using visualizations and summary statistics. 👉🏻 Comment « data » to get the link to the repo and portfolio strategies! #data #students #job

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