🎯 Beginners
- Data Pipeline for Weather Data: Build a data pipeline that pulls weather data from an API and stores it in a database.
- ETL for Movie Ratings: Create an ETL process that extracts movie ratings from a CSV file, transforms the data, and loads it into a database.
- Data Warehouse for Sales Data: Design and implement a data warehouse to store sales data from an e-commerce site.
- Data Pipeline for Social Media Data: Build a data pipeline that ingests social media data from the Twitter API and stores it in a database.
- ETL for Financial Data: Create an ETL process that extracts financial data from an Excel file, transforms the data, and loads it into a database.
- Data Pipeline for News Articles: Build a data pipeline that pulls news articles from an API and stores them in a database.
- ETL for Social Media Analytics: Create an ETL process that extracts social media data, transforms it, and loads it into a database for analytics.
- Data Lake for Satellite Imagery: Build a data lake to store satellite imagery for remote sensing applications.
- Data Pipeline for Financial News: Create a data pipeline that pulls financial news from an API and stores it in a database.
- ETL for Sensor Data: Create an ETL process that extracts sensor data from a CSV file, transforms the data, and loads it into a database.
🎯 Intermediate
- Data Pipeline for Clickstream Data: Build a data pipeline that ingests clickstream data from a website and stores it in a database for analytics.
- ETL for Customer Data: Create an ETL process that extracts customer data from a CRM system, transforms the data, and loads it into a database for analytics.
- Data Lake for Machine Learning: Build a data lake to store data for machine learning models, including training data, feature sets, and model outputs.
- Data Pipeline for Financial Transactions: Create a data pipeline that ingests financial transaction data from a bank’s API and stores it in a database for analytics.
- ETL for Social Media Influence: Create an ETL process that extracts social media data and computes a social media influence score for individuals.
- Data Warehouse for Healthcare Claims: Design and implement a data warehouse to store healthcare claims data from multiple sources for analysis.
- Data Pipeline for IoT Sensor Data: Build a data pipeline that ingests IoT sensor data from multiple sources and stores it in a database for analysis.
- ETL for Stock Market Data: Create an ETL process that extracts stock market data from multiple sources, transforms the data, and loads it into a database for analytics.
- Data Lake for Genomics Data: Build a data lake to store genomics data, including gene sequences, gene expression data, and other genomic features.
- Data Pipeline for Social Media Advertising: Create a data pipeline that ingests social media advertising data and stores it in a database for analysis.
- ETL for Customer Reviews: Create an ETL process that extracts customer review data from multiple sources, transforms the data, and loads it into a database for analysis.
- Data Warehouse for Marketing Data: Design and implement a data warehouse to store marketing data from multiple sources, including web analytics, advertising campaigns, and CRM data.
- Data Pipeline for Traffic Analysis: Build a data pipeline that ingests traffic data from multiple sources, including GPS data and traffic sensors, and stores it in a database for analysis.
- ETL for Social Network Analysis: Create an ETL process that extracts social network data from multiple sources, transforms the data, and loads it into a database for analysis.
- Data Lake for Log Data: Build a data lake to store log data from multiple sources, including web servers, application servers, and network devices.
- Data Pipeline for Supply Chain Optimization: Create a data pipeline that ingests supply chain data from multiple sources and stores it in a database for analysis and optimization.
- ETL for Energy Consumption Data: Create an ETL process that extracts energy consumption data from multiple sources, transforms the data, and loads it into a database for analysis.
- Data Warehouse for Inventory Management: Design and implement a data warehouse to store inventory data from multiple sources, including point-of-sale systems, warehouses, and distribution centers.
- Data Pipeline for Fraud Detection: Build a data pipeline that ingests financial transaction data from multiple sources and performs fraud detection using machine learning.
- ETL for Natural Language Processing: Create an ETL process that extracts natural language data from multiple sources, transforms the data, and loads it into a database for analysis and natural language processing applications.
