The term ‘Data Science Community’ have been thrown by guests a couple of time during the past episodes of Super Data Science Podcast. I can’t help but let it take over this episode.
So, today, we will be talking everything about the Data Science Community – who belongs to it, what we love about it, how we could give back to it and many more.
Who belongs to the Data Science Community? We’re not just talking about the data experts, data engineers, data architects, data scientists, data analysts and so on. The community extends to the enthusiasts, newbies, students, self-learners, etc. People who have used data, algorithms, classes, software, e-books and other resources that were made online by the ones who are equipped with much knowledge skills about data science.
It’s amazing how much resources are available online. And, just when you thought these were already enough, the data science community offers more help. Everyone in the community is always of a helping hand to each other. If you don’t understand something about a new topic you are learning, post on Linkedin, Facebook, etc. and you’ll have your answer in no time. If you think you’re not updated with the current situation in the field, there are a lot of data science people out there who share blogs, videos, and articles everywhere so all you have to do is to find them. If there’s something wrong with your algorithm, colleagues will be more than happy to go through it and discuss it afterwards for you. There’s too much to be thankful for as a member of this community. Everyone just wants see each other grow.
Now, let me ask you this: have you ever shoot them a message and tell them how great of a help they were?
I hope you have. It feels good to show appreciation and also, be appreciated. As a class instructor myself, simple messages I receive from students are the things that aren’t forgotten and very valuable to me. It’s nice to know that I have made an impact and honestly give me motivation to extend my reach to help more. It would be nice to know also that you might be the one who’s making a great impact in the community in the future.
Try it! Message and thank them. A little appreciation for those who have helped you, especially the Data Science Community, wouldn’t hurt!
DID YOU ENJOY THE PODCAST?
- Have you given or received something impactful to and from the data science community lately?
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- Music Credit: Radiate by ElementD & Chordinatez (feat. Mees Van Den Berg)
This is FiveMinuteFriday, episode number 182, the data science community.
Welcome back to the super data science podcast, ladies and gentlemen. Very excited to have you back on the show. And today, I'd like to reiterate something that has been mentioned repeatedly on the podcast in the past three or four episodes. And that is the concept of the data science community. And rarely do I hear something on the podcast mentioned over and over again. Sometimes it happens, and books are mentioned, or techniques and models and algorithms or other concepts are mentioned, but they're usually spread out. But here, we had literally three or maybe even four podcasts where integral people were saying the same thing on and on, and on again. And different people from different countries, different walks of life, different areas of data science. And I wanted to just reiterate that so that we see the true value in this.
So Gregory Piatetsky-Shapiro, Zach Larcher, Matt Corry and I believe Tim Lafferty as well all mentioned the concept of the data science community, and how this community that we're all part of is unique. How, in the space of data science, everybody's extremely friendly. Everybody will always help you out. Wherever you post a question, whether it's on Stack Overflow and GitHub, or whether it's on the Tableau forums or on Kaggle competitions. Wherever you post a question, people will jump in, help you out and provide some advice, provide some guidance, provide some examples, case studies of how they've done things.
Where everybody's quite open to sharing their thoughts, their ideas about developing opensource software. Where people are also expressing ideas about developing algorithms and creating new packages and once those are created, those are also shared and we can use them when we constantly see new R packages, new Python packages, new developments in the space on TensorFlow, in the space of PyTorch and other areas of data science and analytics.
So I just want us to all, for a second, appreciate how lucky we are that we are part of this amazing community that we can learn from all the people that are sharing their videos on YouTube, from all the people that are posted blogs on LinkedIn, or on Medium or in other sources, for all the free eBooks and all these other components that help our education in the space of data science and propel our careers forward. So I'd like for all just pause for a second to acknowledge that and appreciate that we're part of this
And then, something that I'd like to ask of you this weekend. It's a small ask, but I think it's going to make a big difference for the community in general. Just this weekend, think of something that you received or learned from the data science community, whether it was some advice, some help, maybe a blog post, a YouTube video, a comment somebody made. And think of one that actually really impacted your learning pathway, or what you chose to learn next, or how you chose to go about your career, or maybe a certain tool or technique. And first thing, I'll probably encourage you to tell that person who helped you that they did help you. Because so many blog posts, so many videos, so many advice and comments, they go unnoticed. And it doesn't take much to go back there and just post a quick message to say thank you, or if it's on Medium, give that person a few claps, or something else. But probably a message, a personal message is always better, because the person will know they're appreciated, and that will encourage them to share more.
And coming from a perspective of an instructor, where I teach this, as myself, it means the world when you hear a great comment when your content has helped somebody go forward, or your content has helped somebody empower them in their career and change their life. So highly, highly encourage doing that. And the second thing would be to think of a way where you can give back to the community. So if you have never posted a blog post, or never shared a video, or never shared some source code or some code that you were working on, or maybe even just shared a link on LinkedIn, something that you read recently, something that's useful, then this weekend is the time to do it. This is your chance to give back to the community and to show the world that you are part of this data science movement and that you are helping others. And that will, trust me, that will have ripple effects and others will see it, and others will want to share. And you will feel so great when somebody says thank you. And imagine if you can impact somebody's life and help them make the better choice, or make a better choice in their career.
So if you can write a blog post, write a blog post and share it on LinkedIn. If you can record a video, record a video and share it on YouTube. If you don't have time, which can also be the case, but maybe you've read something recently somewhere. Well, just take that link and add a few of your comments to it and share on LinkedIn. It'll take you five minutes, but it can change somebody's life. So let's make this weekend the weekend of the data science community and let's all share something. At least one thing, and at least one thank you to somebody who's helped you. This will be epic. Let's go do it.
All right guys. Have a good weekend, and I'll talk to you next time. Until then, happy analyzing.