Models are only as good as the data they learn from, and most of an AI engineer's time goes into getting data ready. This module covers the core Python data stack: NumPy for fast numerical arrays, and Pandas for loading, filtering, grouping, merging, and cleaning real datasets with missing values, duplicates, and outliers.
You'll also learn to explore data visually with Matplotlib, and to query it with SQL (tables, joins, aggregations, and subqueries), since much of the data you'll use lives in a database rather than a CSV file.
Watch the lessons in order, then continue on to Machine Learning.
A beginner course covering Python basics and core data structures, Jupyter Notebooks and Anaconda, then NumPy, Pandas, and Matplotlib, ending with a small COVID-19 trend analyzer app.
Seven hands-on projects: DataFrame basics, filtering and sorting, grouping, merging, and cleaning a scraped dataset with nulls, duplicates, and outliers.
The basics of Matplotlib and how to apply it to real-world data, with the source code and a sample dataset to follow along with.
Creating and structuring tables, CRUD operations, queries and filtering, aggregations, subqueries, normalization, joins across tables, and performance optimization.