Last updated December 2019

We are on a mission to get the best content relevant to data science for everyone. One of the challenges with any content platform online is having a dedicated and curated list of resources which cater to specific areas in Data Science. We recognize the pain of searching across the web trying to access and understand relevant resources – be it for research, study, work or your own personal projects.
This is why we would like to create a series of Collections, Columns and Compilations, which gathers exciting and diverse viewpoints on different aspects of Data Science. This can include anything ranging from tutorials, hands-on real-world examples, concepts, techniques, methods and domains which are of course relevant to Data Science, Machine Learning, Deep Learning and Artificial Intelligence.
Our Collection
Our collections gather articles focusing on five exciting areas in data science:
- Data Science in the Real World. What are the most pressing problems in data science and what can we do to solve them?
- Data Journalism. How data science helps us understand the world and the media.
- In-Depth Analysis. Featuring interesting and complete (end-to-end) data analysis.
- Inside AI. How AI works, what problems can it solve, what are the potential risks, and what does it mean for the rest of us?
- Sports Analytics. The intersection of data science and athletics.
Our Columns
Our columns are a series of posts written by a certain author that explains a special aspect of their work or dives deep into an idea they would like to explore over several months. Find here some examples:
- Our Writer’s Guide by TDS Team
- Light on Math Machine Learning by Thushan Ganegedara
- Understanding Feature Engineering by Dipanjan (DJ) Sarkar
- The Reality Project by Will Koehrsen
Our Compilations (for the TDS beginners)
Our compilations are for those just getting started with our Medium publication. They can use our compilations to understand the most fundamental topics.






We hope that creating a curated series of collections, columns and compilations will help us showcase the excellent content produced by our talented authors. This, in turn, will help more people understand and demystify Data Science.