#66DaysOfData Round 5 Recap

Jack Raifer Baruch
3 min readMar 14, 2022

Back in 2020, I read a small post by a fellow Data Scientist named Ken Jee, where he spoke about the #66DaysOfData challenge for the first time. At this time, it was the middle of the pandemic, I had been working as a Data Scientist for a few months, was still learning about it (and still am, as anyone in the industry will remind you constantly), and the idea of being part of a movement, of a group, a community, who would help motivate me to reach higher each time.

Well, it has now been about 2 years, 5 rounds, several hundred hours of reading, studying, testing, listening and video watching, 300+ posts, articles in my blog and as of the end of this round, 7 #YouTube videos in my channel. And, since I am at the end of this firth round and I have no idea if and where there will be a 6th (might wait for Ken to catch up), here is the recap of this latest round.

Starting a round with a clear objective in mind is always good, in my case, this round was going to be all about creating content. The goal was to launch my YouTube Channel and creating at least 3 videos. Here is my initial planning for this round (you can access the blank #66DaysOfData Planner by clicking right here):

How did I do?

Really well, and not as good as I would have liked.

Let us dissect this into the good, the complicated and the lessons for the future.

The Good

I more than accomplished my objective, since I did launch the channel, promote it (even if just a bit) and was able to upload 7 videos instead of 3. In truth, there was one video I had been working on before the start of the challenge, then 3 new videos and then the translation into Spanish of the same 3 videos with some small changes.

Still, I feel it was a successful round, reached a bit over 100 subscribers on YouTube, hence was able to give the channel a named URL. Also, my Twitter account keeps growing and passed the 1,000-follower mark during this challenge.

The Complicated

This should be “the bad”, but “the complicated” sounds much more dramatic, and who doesn’t like a bit of drama in their lives.

In short, creating content is hard. From figuring out what to talk about, to recording, to editing, to fighting with the microphone, fumbling with the greenscreen, struggling with the digital greenscreen, and the most difficult part, having to listen to yourself mess up over and over again, until you are able to listen to the tiny and very reasonable voice inside that tells you: “It’s good enough, you will get better with time”.

The Lessons

And that is the biggest lesson of the round. Everything takes time, and nothing will never be perfect. Heck, it won’t even close to as good as what you will already find out there. And the reason is simple, all those content creators have been doing this for years now, you have been doing it for just a few months.

Same thing applies to learning Data Science, it takes a lifetime to be at the top of the field and many years just to feel you are on par with your peers.

But keep moving forward, keep at it, and only compare yourself at different points. For me, at the beginning of the year I had no videos, no subscribers, and no idea how to do it. Now, I have something, and with that little something, I can build something else, a little bit better this time.

What are you waiting for? Go. Get started. Just keep moving forward.

And if you are learning Data Science, join the #66DaysOfData challenge, it is an amazing and very supportive community.

Feel free to connect with me on Twitter or LinkedIn and drop me a note if you have any questions. Also check out my YouTube channel and if you like this article, follow me right here on Medium.

Jack Raifer Baruch
Data Scientist

Email: jackraiferbaruch@gmail.com
LinkedIN: https://www.linkedin.com/in/jackraifer
Twitter: https://twitter.com/JackRaifer
Medium: https://jackraiferbaruch.medium.com
YouTube: https://www.youtube.com/c/JackRaiferBaruch

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Jack Raifer Baruch

Making Data Science and Machine Learning more accessible to people and companies. ML and AI for good. Data Ethics. DATAcentric Organizations.