Data is Not Oil. It is Land.
Note: This blog was written by Dr. William Goodrum and originally posted on www.elderresearch.com. ____________________________ It has become common to talk about data be...
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Using Python and R to Load Relational Database Tables, Part I
I enjoy data prep munging for analyses with computational platforms such as R, Python-Pandas, Julia, Apache Spark, and even relational databases. The wrangling cycle prov...
7 Steps to Ensure and Sustain Data Quality
Several years ago, I met a senior director from a large company. He mentioned the company he worked for was facing data quality issues that eroded customer satisfac...
How to deal with missing data
Originally posted by Vincent Ajayi. The most common challenge faced by data scientists (DS) and data analysts (DA) is missing data. Every day, both DA and DS spend seve...
First steps with leading data visualization programs.
Article was originally published on author’s blog, here. Learning to use data visualization programs Imagine spending countless hours analyzing your data and findin...
Free book: The #dataengineering cookbook by Andreas Kretz
I found an interesting, free book which is still a work in progress book – The Data Engineering Cookbook I will be contributing through the author (Andreas Kretz.com...
5 Reasons You Need a Better Data Management Solution
Congratulations. It took time, but you’re data-driven. You’ve got data scientists to crunch numbers, a fully stocked data lake, and analysts to make it all make sense...
How Big Data can help your Business Analytics?
Business analytics is here to stay, and most ventures are wrapping their heads around the idea. Numerous data gets collected from various interactions among clients and p...
What are the prerequisite skills required to become a data scientist in 2019?
According to the “Future of Jobs 2018” report, it is said that with advancement in machines and artificial intelligence it is likely that these technologies will repl...
Seeing the AI/ML Future in Healthcare Through the Eyes of Physicians. It Doesn’t Look Quite the Way Data Scientists See It.
Summary: If you want to understand the promise of AI/ML in healthcare you need to see it through the eyes of physicians, the ultimate users. Turns out these folks aren�...