data science

Function Pipelines for Mapping Complex Transformations

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

The Computer Vision Pipeline, Part 4: feature extraction

From Deep Learning for Vision Systems by Mohamed Elgendy

In this part, we will take a look at feature extraction—a core component of the computer vision pipeline.

Combining Human and Machine Intelligence

From Human-in-the-Loop Machine Learning by Robert (Munro) Monarch

Maximise Customer Retention

From Fighting Churn with Data by Carl Gold

Is my Problem a Graph Problem?

From Graph Databases in Action by Dave Bechberger and Josh Perryman

In this article, we’ll review what makes a problem a good graph use case. We’ll start by examining a few general categories of problems and discussing why they might make for good graph use case.  Finally, we’ll analyze a general framework that we can use to help us decide if our problem is a good graph use case.

The Data Scientist’s Survival Guide

From Build a Career in Data Science by Emily Robinson and Jacqueline Nolis


Want to Learn Machine Learning Inside Out?

From Grokking Machine Learning by Luis G. Serrano


Working with Large Datasets Faster: using the map function

From Mastering Large Datasets by JT Wolohan

This article explores using the map function creatively in a data project.

Tidying, Manipulating, and Plotting Data with the tidyverse

From Machine Learning with R, tidyverse, and mlr by Hefin Rhys

This article covers:

• What the tidyverse is
• What’s meant by tidy data
• How to install and load the tidyverse
• How to use the tibble, dplyr, ggplot2 and tidyr packages of the tidyverse

Data Analytics on Azure

From Azure Storage, Streaming, and Batch Analytics
By Richard L. Nuckolls

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