All Hands on Data #15
Tomorrow begins September which can only mean one thing.... Pumpkin Spice Season! Grab yourself a PSL and enjoy these articles from our team
The real cost of bad data
An interesting thing the article highlights is that bad data has a cost - a literal dollar cost - and this cost compounds the further it leaks into company business operations. Catching errors and ensuring data integrity not only improves customer experiences but bolsters bottom line financials. - John Forstmeier
What is a Data Quality Framework and How to Implement it?
Data quality is incredibly important. This articles gives a simple outline on how to implement a data quality framework into your system. - Jon Davidson
The Benefits of Natural Language AI for Content Creators
As someone who creates content for their job, I haven’t really thought about using Natural Language AI as apart of my work. This article walks through some ways that it may be helpful. - Steven Johnson
Setting up a local development environment for python data projects using Docker
I’m starting to learn about Docker and Docker Compose since we them at Shipyard. A container is an exciting simple concept made into incredibly agile and powerful tool. This post provides a nice intro into how to config and use Docker containers to text your Python code. - Katt Baum
Machine Learning Systems versus Machine Learning Models
The idea of a model-centric bias is interesting. It would be like a production-centric bias in software. It’s important to build a good foundation where the value add is a by-product of everything that comes before it, and this article puts an ML lens on that thought. - Eric Elsken
How to gather requirements for your data project
As you do work in the data field, it is easy to jump into a project and start finishing up task and forget about some of the important work that should be done on the front end. This article walks through how to gather requirements when working on a project. - Steven Johnson