10 Data Science Subreddits Every Tech Enthusiast should follow
Reddit has become a one-stop platform for reading on almost any topic, and data science is no exception. ‘Subreddits’ on Reddit have become a norm just like any other online discussion forums out there. If you are a frequent Redditor and are always curious about data science, chances are that you will get to know more about this field on the ‘front page of the Internet’ than the whole of world wide web.
Here we list top 10 subreddits that are engaging when it comes to data science. They are presented in no particular order.
This is one of the best subreddits on data visualisation. The reason it is listed here is because visualising data is crucial in data science. At times, without an imaginative mindset, it may be difficult to come up with analytical solutions. This subreddit offers plenty of discussion on visualisations — be it charts, graphs or maps. Threads or questions span eccentric topics right from economics and geography problems to the frequency of search keywords for a TV show.
As the description says in the subreddit, this is ‘a place to share, find, and discuss datasets’. Users can post their datasets and ask for tips or suggestions on improving them. In fact, sharing among data scientists can help bring out innovative solutions to unique problems encountered in the subject.
With programming key to developing data science projects, it is suggested to master this skill gradually in the learning process. To specifically say, Python programming has emerged to be the hot favourite among data science experts due to it being significantly simpler compared to other programming languages like R, Java, C++ etc. Beginners can browse through a variety of threads in this subreddit to get to know the ins and outs of Python. On the other hand, a more experienced programmer can go through another subreddit called r/Python to dig deeper into this language.
Just like statistics, machine learning forms an essential part of data science. With so much going around this area, it is evident to keep track of the latest information and discussions. This is exactly where this subreddit falls. Users share tips, tricks, concepts and even help with implementations in learning ML. An active community, r/machinelearning is a must to follow for ML newbies.
Again, this is a subreddit that is mainly focussed at ML amateurs. Here, threads have discussions revolving basic concepts in ML. In addition, news and interesting ML articles are also shared by users. Sample codes are also shared and discussed.
Data science is vague if the technology is not considered. Anyone passionate in data science should also have fixated interests in technology. As it mentions, ‘for all things technology’, r/technology covers most happenings on the latest tech. Discussions range from everyday tech news to gadgets to global impacts in this field.
DS is incomplete without mentioning deep learning. A sub-field in ML, this subreddit has threads very specific to deep learning applications. Redditors post DL concepts, research papers and even events such as webinars pertaining to DL. Since this niche area has picked up pace in the past few years, r/deeplearning is worth following to know more about deep learning.
A slight offshoot in this list, r/singularity has everything related to what is known as the ‘technological singularity’. So, it has threads on topics such as AI, AGI, human augmentation and so on. It looks more on the philosophical side of rising technology. A definite subreddit for DS enthusiasts.
One simply cannot ignore math in data science. This Reddit avenue exchanges views and concepts surrounding mathematics. Redditors here even give simplified versions of complex topics which is the key highlight of this subreddit. If you go deeper, there are even mathematical logic explained for codes in various languages. A definite addition to a data science reading list!
Last, but not the least, is this subreddit. Mainly aimed at analytics professionals and business peeps, r/analytics discusses information in the analysis of data, new skills and more specifically into web analytics. Data science can be explored to see where it can benefit the analytics domain.