We are living in strange times! The Coronavirus pandemic has impacted the "working from office" more than anyone anticipated. While I prefer working from home, this is not the case for everyone. Less experienced Data Scientists are going through tough times because of lack of mentorship. Many people employed in IT are introverts, which makes this even more apparent.
In this article, I am sharing a few resources that you can freely use to ask questions, keep up with new tools in Data Science and join the community of like-minded people.
Here are a few links that might interest you:
- Labeling and Data Engineering for Conversational AI and Analytics- Data Science for Business Leaders [Course]- Intro to Machine Learning with PyTorch [Course]- Become a Growth Product Manager [Course]- Deep Learning (Adaptive Computation and ML series) [Ebook]- Free skill tests for Data Scientists & Machine Learning EngineersSome of the links above are affiliate links and if you go through them to make a purchase I’ll earn a commission. Keep in mind that I link courses because of their quality and not because of the commission I receive from your purchases.
Discord Communities
Discord is the easiest way to communicate over voice, video, and text, whether you’re part of a school club, a nightly gaming group, a worldwide art community, or just a handful of friends that want to hang out.
There are many Data Science Discord servers, so I am listing here the most popular:
- The Data Share is an official Discord community of the Towards Data Science. You could get help on your current project or help others along their learning paths.
- Data Science is an active, cohesive group of Data Scientists from different industries and academia in order to allow everyone to share their domain expertise, discuss problems, explore the many different disciplines of the profession and in doing so build friendships along the way.
Reddit Communities
Reddit needs no special introduction, so I am just listing three most popular subreddits for Data Science:
- r/MachineLearning – Machine Learning community with 1.1 million members
- r/MLQuestions – A place for beginners to ask stupid questions and for experts to help them!
- r/learnmachinelearning – A subreddit dedicated to learning machine learning.
Quora Communities
Quora is a place to gain and share knowledge. It’s a platform to ask questions and connect with people who contribute unique insights and quality answers.
Quora is gaining popularity and consequently, there are more and more Data Science experts on the site. There are two interesting topics to follow or post questions:
FiveThirtyEight
FiveThirtyEight is a Nate Silver’s blog that uses statistical analysis with hard numbers to tell compelling stories about politics, sports, science, economics and culture.
I developed a habit to read a few FiveThirtyEight articles a day instead of reading news sites. A couple of times I got an applicable idea from the article that helped me with the analysis I was working on.
R-bloggers
R-bloggers – is a blog that you should follow if you are using R as your primary language for Data Science. It has R news and tutorials contributed by hundreds of R bloggers. I don’t use R, but I read the blog regardless, just to see what is happening in the R ecosystem.
Machine Learning Mastery
Machine Learning Mastery – MLM is a blog that its primary purpose is to sell Jason Brownlee’s Ebooks. I am an avid reader of the blog for quite some time and Jason does a great job in introducing complex topics in simple terms. It happened a couple of times, that I’ve simply pointed Junior Data Scientists to MLM to refresh their knowledge about a certain topic.
Simply Statistics
Simply Statistics is a blog written by three biostatistics professors from Ivy League Universities. The blog has high-quality posts about a wide variety of topics – which are all supported by numbers. I only read (a better word would be "study") the ones that are most applicable to my field of work.
Courses
Learn how to train a Machine Learning model and deploy it to a cloud (disclaimer: I am the author of the course).
Before you go
Follow me on Twitter, where I regularly tweet about Data Science and Machine Learning.
