Big data tools are driven by code. Whether you are cleaning a file, querying a database, or logging in to a server to check on a job, you will use a handful of everyday skills again and again. This module covers the ones every data engineer relies on: Python, SQL, and the Linux command line.
You'll start with Python for data analysis and learn how to load, clean, and explore data with pandas. Then you'll move to SQL, the standard language for asking questions of structured data, and finish with the terminal, where most big data systems actually run.
You don't need to master everything here. The goal is to be comfortable enough to follow the rest of the course. Once you've finished it, move on to Distributed Storage & File Formats.
A beginner-friendly tour of the Python data stack: NumPy, pandas, Matplotlib, and Seaborn, from reading data to cleaning and visualising it.
How to install pandas, load data into a DataFrame in a Jupyter Notebook, and take a first look at what you've loaded.
The core of SQL: selecting, filtering, joining, and aggregating data, with technical interview questions solved along the way.
The commands you need to move around a server, manage files, and work from the terminal, where most big data tools run.