Why?
Insight that cannot be communicated is wasted. Good visualization is how analysts explore data and persuade others, and Python is the standard tool for it.
What?
Introduction to data visualization and the data science process, Python basics, NumPy and pandas for data acquisition and preparation, plotting with Matplotlib (line, area, histogram, KDE, bar, pie, box, scatter), and advanced charts such as heat maps and tree maps.
Curriculum:
Introduction to Data Visualization
Why visualization matters, the data science process flow and the role of visualization in it.
Python Basics
Benefits of Python for data science, Jupyter Notebook, imports, lists, tuples and dictionaries.
Python for Data Science
The NumPy library, data acquisition and preparation, and the pandas library.
Visualization Basics
Matplotlib architecture and the core plots: line, area, histogram, KDE, bar, pie, box and scatter.
Advanced Visualization Tools
Heat maps and tree maps.
Notes
Lab oriented, with Jupyter notebooks. Beginners should do the Python basics chapter on their own.