Python for Data Analysis & Visualization

Data is the new oil, and Python is the refinery. This course teaches you to extract insights from raw data using Python's powerful ecosystem of data analysis and visualization libraries.

We start with Python fundamentals tailored for data work: lists, dictionaries, comprehensions, and functions. You'll understand how Python's design philosophy makes it ideal for exploratory data analysis and quick iteration.

NumPy is your first major library—you'll master array operations, broadcasting, and vectorized computations that make Python fast. Understanding NumPy is essential because it underlies nearly every data science library in Python.

Pandas is the workhorse of data analysis. You'll learn to load data from CSV, Excel, SQL databases, and APIs. Master DataFrames and Series, selection and filtering, grouping and aggregation, merging and joining datasets, handling missing data, and reshaping with pivot tables.

Data visualization brings insights to life. With Matplotlib, you'll create publication-quality figures: line plots, bar charts, scatter plots, histograms, and subplots. Seaborn builds on Matplotlib with beautiful statistical visualizations and sensible defaults.

Real-world projects drive learning: you'll analyze sales data to identify trends, explore demographic datasets to understand populations, and build interactive dashboards. By course end, you'll confidently approach any dataset, clean and transform it, and communicate findings through compelling visualizations.