Local Data Analysis: Querying CSV, JSON, and Parquet Files

Analyzing data often means dealing with raw files like CSV, JSON, and Parquet before they ever reach a database. Learn the fastest and most efficient ways to handle this data locally. This course teaches you how to bypass complex ETL processes and server setup, giving you the power to perform quick exploratory analysis and data cleaning directly within a local environment. You will gain confidence in handling large datasets and complex nested structures efficiently. What you'll learn: * Understand the structural differences and performance trade-offs between CSV, JSON, and columnar Parquet formats. * Apply efficient techniques for reading and loading large datasets without exhausting local memory resources. * Practice cleaning and normalizing data, including handling missing values and performing necessary type conversions. * Master filtering, sorting, and aggregating data using modern vectorized operations for maximum local performance. * Configure basic structures to parse and flatten semi-structured JSON data into usable tabular formats. The course begins by defining the characteristics of each file type and the best practices for handling them. You will then move through practical, written exercises focused on reading, cleaning, and querying data using modern libraries. This course is designed for absolute beginners: aspiring data analysts, developers, and anyone who needs to quickly analyze data stored in common file formats. No prior experience with databases or server setup is required. Start mastering local data analysis today and unlock immediate insights from your raw files.