From Build Your Career in Data Science by Emily Robinson and Jacqueline Nolis
From Learn Quantum Computing with Python and Q# by Sarah C. Kaiser and Christopher E. Granade
From Mastering Large Datasets by JT Wolohan
This article explores using the map function creatively in a data project.
From Data Science at Scale with Python and Dask by Jesse C. Daniel
This article delves into building linear models using Dask-ML.
From Code Like a Pro by Dane Hillard
Six Questions for David Kopec, author of Classic Computer Science Problems in Python
David Kopec is Assistant Professor in computer science at Vermont’s Champlain College and author of two books in the Classic Problems series. If you want more, find @davekopec on Twitter.
From Classic Computer Science Problems in Python by David Kopec
A large number of problems which computational tools solve can be broadly categorized as constraint-satisfaction problems (CSPs). CSPs are composed of variables with possible values which fall into ranges known as domains. Constraints between the variables must be satisfied in order for constraint-satisfaction problems to be solved. Those three core concepts—variables, domains, and constraints—are simple to understand, and their generality underlies the wide applicabilit
Chapters selected by Naomi Ceder
With Naomi Ceder, author of The Quick Python Book, Third Edition.
Naomi Ceder earned a Ph.D in Classics, but, since 2001, has been learning, teaching, and using Python. An elected fellow of the Python Software Foundation, Naomi currently serves as chair of its board of directors. She also speaks internationally about the Python community, and on inclusion and diversity in technology in general. By day she leads a team of Python programmers for Dick Blick Art Materials, and in her spare time she enjoys sketching, knitting, and deep philosophical conversations with her dog.
