How To Use Twitter to Learn Data Science (or anything)

When I decided that I wanted to become a data scientist, I started following some data scientists on twitter to see what they talk about and what was going on in the “industry”. Then I saw them pointing one another to resources and answering each other’s questions, and I realized I had only seen the tip of the iceberg of “Data Science Twitter”. That’s when I created a new twitter account.


A few things I should say first…. I think “data science” can be replaced by just about any other topic, but especially science & tech topics, so please keep that in mind as you read this. I follow a bunch of scientists on my “regular” personal twitter account @paix120, and I sense the same things going on in their communities as I’m about to outline for data science.

Another thing I want to mention is that I’ve had other “topical” twitter accounts. I created one called @womenwithdroids when I started a blog of the same name, and I was amazed at how many awesome women I met that were building android apps, wanted to learn more about how to use their android phones (which at the time were being marketed as a “manly” alternative to the “cutesy” iPhone), and wanted to join a community of women talking about android phones and apps. At the time, I had created a separate account because I saw it as a “business” account for my blog, but I realized that there was a lot of value in separating that from my personal account. I’ll go into that below. Now that you know a little background, let’s dive into how you can use twitter to learn just about anything.


I have explained to people I meet in person how much I gain from Twitter, and they often look at me like I’m a little nutty. I have heard a few recurring comments from them that I see as misconceptions:

  1. “I started using Twitter and was overwhelmed. I couldn’t keep up with my timeline.”

    My answer to that is that first, you’re not supposed to “keep up” with your Twitter timeline. I don’t use Facebook, but I get the impression that people that do will scroll back through every post that happened since the last time they visited, to make sure they don’t miss any important info from their friends. Twitter is not like that.

    On Twitter, you can jump on when you need a 5-minute break from work, read a few tweets, mark some longer stories to read later or go read an article or two now, and then get right back to work. People that use twitter won’t get mad if you miss one of their tweets. If something resonates with a lot of people, it will be retweeted and you will probably see it later. If not, it’s not a big deal. You see what you see when you’re online, and don’t worry about what you may have missed, it will just stress you out.

    Think of Twitter like the news. You may want to see if anything has just happened, what’s at the “top of the news”, or what people are talking about that happened recently. If there is a big news story, it will likely still be visible when you visit later. It would be stressful to try to keep up with every news article that’s published at any time.

    I just scroll back a half hour or so and scroll up until I’m ready to do something else. If I’m looking for tweets about a specific topic, I do a search and see what the top tweets are for it. You can narrow down the search results to “People You Follow” if you only want to see what people you are connected with are saying about the topic.

  2. “I started using Twitter and it was just a bunch of junk I didn’t care about.”

    Twitter has an onboarding problem. The problem used to be that when you started a new account, you weren’t following anyone, then people would feel lost and not know how to find interesting accounts to follow. Then they started suggesting interesting accounts. Now, the onboarding process shows you a whole bunch of “brand” accounts to follow (whether those are celebrities or companies, they are usually accounts generated to gain followers or money), then they also try to get you to import your email contacts and follow all of them. I don’t know about you, but I don’t care much about what celebrities have to say, and many of my email contacts are people that I had a short business exchange with years ago and have no interest in keeping up with now. It’s no wonder people start with an uninteresting and overwhelming timeline.

    My recommendation is that if there is someone in your timeline that frequently annoys you or tweets boring stuff, unfollow them. That’s just clutter. If you see a friend retweet something from someone you don’t follow that is interesting, click on that person’s profile, read a few tweets and see if they are tweeting other things that interest you, and if so, follow them. Constantly tailor your timeline to work for you.

    Another important suggestion is to use twitter lists. If there are certain people that you really do want to keep up with (like personal friends, or a small group of accounts on a very specific topic), put them in a list. You can also follow them in your normal timeline, but you don’t have to. When you click over to your list, you will see only tweets by those accounts. One example of how I use a list on my personal account is my “Harrisonburg businesses” list. I don’t frequently care about whether a local restaurant is having a special, or if there’s a cultural event going on at our local university. However, when I’m looking for something to do one night, I can click over to that list and see what the local businesses are tweeting about today. Are there any cool bands playing in town? A special at a local hangout? I follow very few of those 160+ accounts in my regular timeline, but now I have a collection of them in one place when I do want to scroll back through 24 hours of tweets to find something specific.

  3. “Social Media is a time suck for me, and I don’t want to add any more social feeds to my life to waste time on”

    OK, I can see this. It is easy to get sucked in and spend a lot of time on social media. To me, this is just a reason to optimize your account so it’s beneficial to you. If you’re just reading celebrity gossip and trending topics, are you improving your life? However, if you have a goal to become a data scientist, and you follow accounts that are actually educating you, is it so bad to spend some time “sucked into” a feed that is actually getting you closer to your goal in your “free time” and keeping you up to date on the latest topics that a colleague or future employer may expect you to know about?


Now that I’ve explained some misconceptions about Twitter, I want to explain why I have a separate account for “Data Science Renee”. I have had my personal account on Twitter since 2008. I have only really been into data science since late 2013. I have a “network” of people that I chat with about a variety of topics on my personal account, including political topics and random things that catch my attention. Here are my main reasons for starting a separate account for @becomingdatasci:

  • I personally wanted to separate the topic out. I wanted to go “all in” on data science, and have an account where I ONLY follow people that talk about data science, even people I wouldn’t follow in my normal timeline. I could have done this with a list, but I wanted to take it further than that.

  • I also wanted to be able to tweet like crazy about data science, and not feel like I had to hold back in order to avoid overwhelming my existing followers with a flood of tweets on a new topic. They might unfollow me if I started tweeting 20 times a day about data science when I had rarely mentioned it before, and my new interest might far outweigh my tweets on other topics I’m interested in. I didn’t want to lose that existing network.

  • The opposite is also true. I wanted to be able to connect this new account to my blog, and use it to make work connections, without worrying about including personal political views and tweets about gardening and cute animals in that feed! I also would know that people that follow this account are following it because of data science. I check out my followers on this account more often than I do on my personal account, because they’re more likely to share this particular interest with me.

  • I know I’m good at curating interesting articles about a topic, and I wanted this account to be considered a “go to” account that others could recommend to their friends interested in learning about data science, without worrying what else I might be tweeting about. I decided to become a sort of “learning data science channel”.


So you see why I have separated this account from my existing personal Twitter account, and how I have tailored it to work for me. But what does that mean? What have I actually gained from this twitter account?

  1. I have learned a LOT that I wouldn’t otherwise know about data science. There are terms that I wouldn’t have known to Google that some of the people I follow tweet about and link to articles, academic publications, and tutorials about. There is a constant flow of interesting new information coming out of the data science “industry” so I can keep up with what is being talked about right now and what is considered “state of the art” and exciting to other data scientists. It’s like being able to walk around and listen in on lunch tables at a data science conference. Everyone is talking about something slightly different, but all in the general topic of data science, and each person is honing in on what is interesting or exciting to them within this realm.

  2. I have made connections that I wouldn’t have made otherwise. I don’t have a lot of time or money to constantly travel to data science conferences and meet people in person. I live in a small town and there aren’t a lot of other people talking about data science here (yet). Twitter has given me a way to personally connect with other data scientists. I have connected with some that don’t live far from me, after all! I have connected with many that live in other countries that I likely wouldn’t even meet at a conference. These connections have cheered me on in my learning, connected me to resources, and more!

  3. I have become a “face” of a person learning data science. At once conference I did attend, I was recognized as “Data Science Renee”! I have been asked to be interviewed on podcasts and blogs (some of those should be coming up soon), offered contract work, and offered free admission to a conference I unfortunately couldn’t go to, but was excited to be considered for. “Famous” people in the industry are now coming to me to work with them in some way. New learners seem to look to me as a resource and guide, and want to see how I learned what I know, and how I have struggled, so they can compare that to their own experiences.

  4. I have found many other women working in data science. When I was first learning about data science, all of the “who’s who” lists of people to follow, people that were interviewed for books or other resources, and the “faces” of data science were often white or asian men, with maybe one woman or minority included in the group. (This is typical of the tech industry.) However, as I made more and more connections, and started to seek out women and other minorities in the industry, I have been able to connect with them and learn from them and hopefully amplify their voices. I now have a twitter list with almost 450 women that work in data science or statistics, and now that list can be a resource for other women looking for role models like them in the industry!

  5. I have learned some specific data science tools and techniques. I regularly see great tutorials on twitter, via blog posts or videos or github links, that show me how to do something I have wanted to learn how to do. These would often be hard to find by searching, but come right to me in my twitter feed where I can bookmark them for later learning sessions.

  6. People on twitter have reached out to help me solve problems when I’m stuck. I have received tweets from people that built python packages I was using, people that had resources that could help me, or just people with general advice and feedback! If I’m clear about what I’m doing and where I’m stuck, I now have a strong enough follower base that I will almost always get a helpful answer!

  7. Not only do I find out about resources I wouldn’t otherwise have, but I see opinions of others on existing resources. A conversation on twitter about being overwhelmed by the vast amount of things there are to learn in the broad topic of “data science” helped inspire me to bring an idea I had been having to life. I have taken a course online that got really difficult at about the 5th lesson. I didn’t know whether it was just me and I had hit a roadblock, or if a lot of people found that course difficult and I just needed some outside resources to continue with it. I also often don’t know where to start in my long list of bookmarked “things to learn”. But seeing what people tweet about, and how others have learned, really is helping me on my learning journey. You can read about my new website DataSciGuide here. I’m hoping the ratings (and eventually learning guides and a recommender system) there will help others avoid “data science learning overwhelm”. (P.S. I’m now in the phase where I need reviews on the items I’ve posted, so please go rate some things!)


Hopefully this post has helped you understand how to use Twitter to join a community and learn something you have been wanting to learn! You can really gain a lot from it if you optimize its benefit to you like I have.

I know the question now will be, “so who are the best people to follow on twitter for data science?,” and I’m hesitant to answer that for you since there are so many people out there, some with specific topics that would be better for you personally than what I would recommend. For instance, maybe you are especially interested in learning data science for sports analytics, which is a specific topic I don’t follow many people on.

If you follow me on @becomingdatasci and see who I retweet, you’ll find people that are sharing resources that I think are beneficial, so you can start there. You can’t go by my twitter favorites since I use those as bookmarks and haven’t read many of them yet. You could look through people I follow, but there are a lot of them, and they’re not ranked in a helpful way. You can also follow the list of data science women I mentioned above.

Others that are often good to start with are people with data science blogs, since they’re usually purposely writing to educate others. Here’s a large list of data science blogs that includes the twitter handle of the author or blog where applicable, and is sorted into categories. Check it out!


So to recap:

  1. Tailor your twitter timeline frequently. Unfollow those that annoy or bore you, and follow new accounts on topics you want to know more about.

  2. If you seriously want to hone in on one topic, or to become a “channel” for a topic, create a separate account for it

  3. Use twitter lists to create small lists of people you especially want to keep up with, or sub-specialty topics you occasionally want to dive into. You can follow accounts in lists that you might not otherwise follow in your timeline.

  4. Actually connect with other people. Find people like you that can be role models for your learning. Ask them questions. Help others out when they ask questions on a topic you know more about. Join the community and the conversation.

  5. Have fun and don’t get overwhelmed! Use others’ opinions and recommendations to carve out your learning path.

Comment below if you have any questions about using twitter to help learn data science!


  1. David Meza
    May 11, 2016

    This is a great blog topic. I think it shows people the value of Twitter if used correctly. Like you I focus on data science in my feed. I find it helpful and provides me with great insight into the field. Thanks for the explanation on lists, I have been meaning to look up their use.

    • Renee
      May 30, 2016


  2. Sahil Dawka
    May 13, 2016

    Hey great article! Everything is a double-edged sword, so this is like an avoid-gutting-yourself article. :D

    I’m starting to get interested in this subject but I wonder if it is saturated? How long do you think it would take to get up to speed, giving an engineering background in Nanotechnology, some coding experience, and a lot of curiosity?

    • Renee
      May 30, 2016

      Ha, true. No, Data Science is definitely NOT saturated! If anything, there is a glut of people suddenly calling themselves data scientists, and a glut of companies listing “data scientist” positions that hope for a “unicorn”, but if you look past all of that, there are still a LOT of companies that need people that can do advanced analytics, and with the amount of data being collected, and the limited number of new analysts being produced by universities, that number will only get larger in the future.

      Yes, if you have a scientific or engineering background and coding experience, you’re already well on your way. As you’ll find out if you listen to the Becoming a Data Scientist podcast, curiosity is one of the key traits for data scientists! Go for it!

      You may want to check out the Data Science Learning Club as you get started! (see links in menu)


  3. Ted
    Feb 3, 2017

    Very good post Renee! I have an engineering background and reinventing my career. I became interested with oracle database and oracle business intelligence 2 yrs ago while on my job search it seemed like this field saturated already and requires at least 6 years of experience.

    But if it wasn’t for Oracle BI, I would not have discovered tableau and other visualization tools. I am looking to learn Python or R but don’t know which of these can get me to speed.

    I’m going to follow your website to help me reach my goal of getting to analytics… it can easily get overwhelming most of the time!

    Do you need to have a Master’s degree to become a data scientist?

    Thanks again!

    • Renee
      Feb 4, 2017

      No, I personally don’t think a Master’s degree is necessary, but it can help. Both to (unfortunately) show “proof” of the skills you may already have, and for the larger companies with HR systems that have cutoffs by things like degree.

      Not to say that degrees are useless – I definitely went a lot further with math in my Systems Engineering masters program than I would have otherwise.

      However, I encourage companies to put things like “masters degree or equivalent experience” if they feel the need to put it. I think real-world experience working with data can be more important than a degree!

  4. subdued reader
    May 8, 2017

    Thank you for writing this article. I, somewhat ironically, saw it on Twitter yesterday, and needed it to break out of my Twitter addiction.