SQL gets data out of a database, but Python is where much of the real analysis happens. This module starts with the essentials: working in Jupyter notebooks, Python variables and functions, and reading data from files such as CSVs.
You'll then learn the two core libraries for data work: NumPy for fast numerical computing, and Pandas for working with tabular data, including filtering, sorting, aggregating, and joining tables. You'll finish by turning results into charts with Matplotlib and Seaborn, following a complete exploratory analysis from raw files to visuals.
Once you can load and explore data in Python, you'll be ready for the next module, Data Cleaning & Wrangling, where you learn to fix messy real-world data.
A quick, practical tour of a Python analysis: reading and profiling data from flat files, joining multiple tables, filtering, sorting, and aggregating with Pandas, and visualizing the results with charts.
A 4-hour course reviewing basic Python, then reading data from CSVs, SQL, and Excel, cleaning and transforming it with NumPy and Pandas, visualizing with Matplotlib and Seaborn, and working in Jupyter Notebook.
A 10-hour, coding-first course covering Python and Jupyter fundamentals, NumPy arrays, Pandas Series and DataFrames, merging and grouping, visualization with Matplotlib and Seaborn, and an exploratory data analysis case study.