Data Ingestion Foundations with Python

Before you can clean, visualize, or model data, you must first master the essential skill of getting that data into your environment. Data ingestion is the critical first step in any analytical workflow, and knowing how to handle diverse file formats is what sets successful data professionals apart. This course provides a comprehensive guide to reading and importing data using Python, ensuring you can work with information regardless of where it is stored. You will gain the skills necessary to transform raw files into structured formats ready for exploration and analysis. By the end of this course, you will be able to navigate file systems and handle various data sources with confidence and precision. What you'll learn: - Understand the fundamentals of file paths and directory management using modern libraries like pathlib - Import data from flat files such as CSVs and text documents using standard Python tools and pandas - Read data from spreadsheet software including Excel files with multiple sheets - Load datasets from specialized formats used in SAS, Stata, and MATLAB - Connect to and query relational databases like SQLite and PostgreSQL to extract data - Apply best practices for handling character encoding and missing values during the import process The course begins with foundational definitions and an overview of how Python interacts with your computer's file system. You will then progress through written explanations and code-based exercises that cover specific file types, third-party software formats, and database connections. This course is designed for beginners who have a basic understanding of Python syntax and want to learn the practicalities of data handling. No prior experience with data engineering or databases is required. Start building your data ingestion skills and unlock the potential of your datasets.