data science

Is my Problem a Graph Problem?

From Graph Databases in Action by Dave Bechberger

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 Your 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 Data Engineering: Real-time, streaming, and batch analytics
By Richard L. Nuckolls


Machine Learning from the Ground up

From Machine Learning for Mortals (Mere and Otherwise) by Hefin I. Rhys


Beyond Beyond Spreadsheets

Six Questions for Jonathan Carroll, author of Beyond Spreadsheets with R

By Frances Lefkowitz

Jonathan Carroll is a data science consultant providing R programming services. He holds a PhD in theoretical physics.

Modern Data Solutions with Python

From Python for Big Datasets by John T. Wolohan

The Magic of Graphs and Machine Learning

From Graph-Powered Machine Learning by Alessandro Negro

© 2019 Manning — Design Credits