From Practices of the Python Pro by Dane Hillard
The article explores the concept of namespaces and how Python uses them to help make code better.
From Zero to AI by Nicolo Valigi and Gianluca Mauro
Six Questions for Jesse C. Daniel, author of Data Science with Python and Dask
Jesse Daniel is a software developer (Python, Scala, JavaScript, C#) who leads a team of data scientists at a media technology company. He has taught Python for Data Science at the University of Denver.
From Practical Data Science with R, Second Edition by Nina Zumel and John Mount
This article discusses how one can experiment with the effectiveness of a classification model using a spam filter.
From Azure Data Engineering by Richard Nuckolls
This article delves into Azure’s tools for data engineering and why you should consider using them.
From Probabilistic Deep Learning with Python by Oliver Dürr, Beate Sick, and Elvis Murina
From Spark in Action, Second Edition by Jean-Georges Perrin
This article teaches you how to perform an aggregation using Apache Spark. You first look at the definition of an aggregation. You may already know and use aggregations in your job, and this might be a reminder for you. If this is the case, you can safely skim through it: Apache Spark’s aggregations are standard. The second part of this section shows you how to transform a SQL aggregation statement to Spark.
From Machine Learning for Business by Doug Hudgeon and Richard Nichol
This article covers basic time-series forecasting: what it is and why it’s a tough problem.
From Mastering Large Datasets with Python by J.T. Wolohan
This article covers
· Using map to do complex data transformations
· Chaining together small functions into pipelines
· Applying these pipelines in parallel on large datasets