Why subscribe to Data Tinkerer?

Data Tinkerer delivers curated, actionable insights about data science, engineering, and analysis from 100+ tech companies and blogs directly to your inbox. So if you don’t want to sift through all of them manually, Data Tinkerer is for you!

So what is the Data Tinkerer newsletter structure

Data Tinkerer has 4 parts to specifically cater to different data needs so you are not spammed with stuff you don’t need:

Data Bytes - Weekly round-up of latest developments in all things AI and data (science, engineering and analysis)

Data Science - Breakdown of a technical implementation of AI/Data Science across top companies

Data Engineering - Deep dive to a technical implementation of Data Engineering across top companies

Data Analysis - Providing explanation for important data analysis concepts and sharing recommended data visualisations

So which newsletter should I subscribe to?

Well that’s up to you but if you just want be across the latest updates or are not technical, best to start with only Data Bytes which is a weekly round-up of the latest news related to AI and data.

If you are more technical, you can subscribe to your relevant field of interest (Data Science, Data Engineering, Data Analysis) and receive technical deep dives and explanations as well

So what’s the difference between the free and paid versions?

Free Version

You get weekly updates of the full articles and they are freely available for 4 weeks

Paid Version

1- Access to a list of data science/data engineering updates from 100+ companies (sample here) that are monitored on a weekly basis. Each of these updates are broken down based on their stream (data science/data engineering), tech stack, company, industry, summary of the update

2- Access to paid community chat where you can discuss work projects or bounce off ideas with other paid members

3- Access to archive of all of the posts

So who are you and why are you doing this?

Who am I? I have tinkered with different facets of data—I’ve been a data analyst, scientist, and engineer, so I’ve worked on spreadsheets, created forecast models, and built data pipelines that worked (most of the time). Why am I doing this? I wanted to be across the latest data developments as a data consultant so was looking for a source. When I did not find a curated source for different data roles, I decided to aggregate and share them here