This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California
San Diego
which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively
especially for data analysis applications.*Certain low-level language features are discussed in detail
especially Python memory management and data structures.*Using Python effectively means taking advantage of its vast ecosystem.*The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules.* The section on object-oriented programming explains features of the language that facilitate common programming patterns. After developing the key Python language features
the book moves on to third-party modules that are foundational for effective data analysis
starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then
the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis
key modules such as Matplotlib are developed in detail
along with web-based options such as Bokeh
Holoviews
Altair
and Plotly.
The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes
which is especially helpful for newcomers to both Python and data analysis.* To get the most out of this book
open a Python interpreter and type along with the many code samples.
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