#Data Collection

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!

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

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

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

Data visualisation book recommendation for anyone who wants to turn data into interactive stories, not just static charts 📊🌍💻 ✨ Teaches you how to move from spreadsheets to web-based visualisations ✨ Covers tools like Google Sheets, Datawrapper, Tableau Public, Chart.js & Leaflet ✨ Perfect if you want to communicate data clearly — even without heavy coding ✨Open-source so freely available online 📌 Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code — Jack Dougherty & Ilya Ilyankou 💭 Summary: This book shows you how to clean, analyse, and visualise data using practical tools — starting with spreadsheets and moving into customisable web-based charts and maps. It’s especially useful if you want to share your work online and make your data interactive, not just informative. If you’re learning data science, bioinformatics, or just want to present your work better, this is a great place to start 🤍 📌 Save this for later — I’ll be sharing more recommendations soon. #womeninstem #datavisualization #datascience #bioinformatics #tech

1. Netflix Show Clustering Group similar shows using K-Means based on genre, rating, and duration. Tech Stack: Python, Pandas, Scikit-learn, Seaborn 2. Spotify Audio Feature Analyzer Analyze songs by tempo, energy and danceability using Spotify API. Tech Stack: Python, Spotipy, Matplotlib, Plotly 3. YouTube Trending Video Analyzer Discover what makes a video go viral. Tech Stack: Python, Pandas, BeautifulSoup, Seaborn 4. Resume Scanner using NLP Parse and rank resumes based on job description matching. Tech Stack: Python, SpaCy, NLTK, Streamlit 5. Crypto Price Predictor Predict BTC/ETH prices using historical data. Tech Stack: Python, LSTM (Keras), Pandas, Matplotlib 6. Instagram Hashtag Recommender Suggest hashtags based on image captions or niche. Tech Stack: Python, NLP, TF-IDF, Cosine Similarity 7. Reddit Sentiment Tracker Analyze community sentiment on hot topics using Reddit API. Tech Stack: Python, PRAW, VADER, Plotly 8. AI Job Postings Dashboard Scrape and visualize job trends by tech stack and location. Tech Stack: Python, Selenium/BeautifulSoup, Streamlit 9. Airbnb Price Estimator Predict listing prices based on location and amenities. Tech Stack: Python, Scikit-learn, Pandas, XGBoost 10. Food Calorie Image Classifier Estimate calories from food images using CNNs. Tech Stack: Python, TensorFlow/Keras, OpenCV Each project can be completed in 1-2 weekends. #datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency #projects

It’s so useful! 🤗 Comment data and I’ll send you the link!

Here are 10 latest project Ideas for Data Analytics 1. Customer Churn Prediction: Predict customer churn for subscription-based services. Skills: EDA, classification models. Tools: Python, Scikit-Learn. 2. Retail Sales Forecasting: Forecast sales using historical data. Skills: Time series analysis. Tools: Python, Statsmodels. 3. Sentiment Analysis: Analyze sentiments in product reviews or tweets. Skills: Text processing, NLP. Tools: Python, NLTK. 4. Loan Approval Prediction: Predict loan approvals based on credit risk. Skills: Classification models. Tools: Python, Scikit-Learn. 5. COVID-19 Data Analysis: Explore and visualize COVID-19 trends. Skills: EDA, visualization. Tools: Python, Tableau. 6. Traffic Accident Analysis: Discover patterns in traffic accidents. Skills: Clustering, heatmaps. Tools: Python, Folium. 7. Movie Recommendation System: Build a recommendation system using user ratings. Skills: Collaborative filtering. Tools: Python, Scikit-Learn. 8. E-commerce Analysis: Analyze top-performing products in e-commerce. Skills: EDA, association rules. Tools: Python, Apriori. 9. Stock Market Analysis: Analyze stock trends using historical data. Skills: Moving averages, sentiment analysis. Tools: Python, Matplotlib. 10. Employee Attrition Analysis: Predict employee turnover. Skills: Classification models, HR analytics. Tools: Python, Scikit-Learn. Each of these projects builds core analytics skills using open datasets and popular Python libraries. If you want to know how to work on this projects with database then comment “projects” and I will share the guide or you can access it from my bio. #dataanalyticsprojects #datascience #dataanalytics #project #trending #viral

🚨 Want to become a Data Analyst but don’t know where to start? 👀 I’ve got you covered — Microsoft has launched a dedicated learning path with free resources to help you master Data Analytics step by step! 📊 💬 Comment “DATA” and I’ll DM you the complete roadmap + official Microsoft resources. ✅ Beginner to advanced topics covered ✅ 100% FREE learning materials ✅ Certificate-ready path to build your career 🔥 This is your sign to start learning data analytics the right way — straight from Microsoft! 🚀

A data warehouse is a single source of truth that helps business functions perform their data analysis operations easier. Here's what a simple data warehouse looks like: 1. Data sources 2. Bronze layer 3. Silver layer 4. Gold layer 5. Analytics There's so much more that goes into a data warehouse (e.g. ingestion frequency, data governance policies, data validation checks etc), but this is a high level design you can start with. Different companies may configure the stages in different ways according to their users' unique requirements, but the generic workflow applies to all! #dataanalytics #dataengineering #datascience #techtok #dejavu

YOUR 20s ARE FOR DATA, NOT DOLLARS. 💾 . We have this twisted idea that if we aren’t “Forbes 30 Under 30” material by age 24, we have failed. . I am 31. And looking back, I realize my 20s weren’t for building the Empire. They were for gathering the materials. . Every bad job. Every failed project. That wasn’t wasted time. That was Data Collection. . You cannot build a house if you don’t know what bricks you like. Stop trying to rush the construction phase when you are still in the design phase. . Share this with a friend who is stressing out right now. ✈️ #mindsetshift #inyour20s #lifeadvice #quarterlifecrisis #careeradvice

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