 What's going on everybody? Welcome back to another video. Today I'm doing my very first reaction video to Kenji's how to go from a data analyst to a data scientist. So Kenji just posted a video of how to go from a data analyst to a data scientist and he posts this kind of cryptic LinkedIn post and it says, I'm sure this video will start another beef with Alex Freeberg and I honestly have no earthly idea what he's talking about. I mean, I love the man like a brother and to think that there's some type of disagreement or beef between us, it honestly hurts. And to be completely transparent, I have planned on doing this exact video in just a couple of weeks. And so I'm fairly certain that Kenji stole my video. So with that being said, we are going to pull up Ken's video and watch through it together. I'll be giving you my thoughts, my comments, and we'll see where it goes. All right, let's start it and let's see where Ken takes us. All right, so that's obviously really funny, but I want to note that I can see all of his tabs really quick and I don't remember any video where I've been able to do that. So I'm going to take a quick look at them. It says entrepreneurship, school, personal scouts, get smarter. I'm guessing that's just memes, DFS recipes. I really want to know what's in those recipes. I mean, I feel like, my God, he's hiding something really good in there. Bitcoin metrics, home tabs and podcasts. All right, I just wanted to make note of that. Let's keep going. Data analyst work and data science work is relatively similar. First, I want to make it clear that a data science role isn't better than a data analyst role. Thank you, Ken. I'm glad that you said that. I have said that many times in different podcasts and Q&As, I personally feel like a data scientist is not better than a data analyst. It's just different type of work using different skills. That's just my opinion. It's simply a position that leverages a slightly different set of skills. Many people, including my friend, Alex, the analyst, prefer the data analyst life. All right. Let's keep going. I think that data science may be more suited for you if you enjoy coding more. Many analysts code, but they leverage other tools like Tableau and Power BI as well. Honestly, data science positions are still rather poorly defined compared to data analyst roles. I'm going to jump in here and say I both agree and disagree. I agree that data scientist roles are terribly defined. I mean, that is a common problem, but I disagree because I feel like data analyst roles are just as poorly defined. And what's funny is you can find a lot of examples of data scientist positions that literally have the job description of a data analyst. But let's keep going. So if you're okay with some ambiguity, that work could also be suited for you more. Now that I've gotten that out of the way, these are some of my practical tips for making the transition from data analyst to data scientist. I'm loving this transition. My first tip is to show employers that you can code. As I mentioned before, data analysts generally program slightly less than data scientists. So he mentioned that earlier. I didn't comment on it, but I'm going to comment on it now. That is very true. If you are not good at coding or you're less inclined to code, I would highly recommend the data analyst route if you still have that inclination. If you want to become a data scientist, you're going to be coding a lot more. If you want to make the transition into data science, you should find ways to showcase your programming ability in Python or R. The best way to do this is with a portfolio on either GitHub or Kaggle.com. Completely agree. And that's what I tell a lot of people. I say, look, if you want to show that, you know, SQL, Python, Tableau, you need to create a portfolio, some type of dashboard, something that you can show employers that you know what you're talking about. If you just say, you know Python, how are they going to know that you actually know the things that are relevant to them? How do they know that, you know, pandas and scikit-learn for data science, or all these other programs or libraries, how are they going to know if you don't show them? So I completely agree with what can I say. For someone with an analyst background, building an end-to-end project is also going to be extremely valuable. I have a few of my videos on projects that I've linked in the card in the top right and below in the description section. Well, that's nice. The next thing you should do is to highlight your strengths. Data analysts often have high levels of business understanding and logic. Can we just stop for a second? And I know sometimes I'm watching for like 30 seconds at a time. I literally have not watched this yet. This is my first time watching this. And so I'm trying to pause at like normal breaking points. But I mean, he's literally flexing on us. And I mean, kudos to you, man. I haven't done pull-ups in a while. And so I'm impressed that you can still do them relatively easily. I don't think I'd be brave enough to put that in a video. So I respect you. You shouldn't try to hide this. Many hiring managers are actually looking for that skill set if you check the box for coding ability. Make sure your resume shows the type of value that you've been creating at your current company or in your projects. Also, make sure to tell these stories during your actual interviews. Yes. I tell, again, I feel like Ken is, he's hitting some of the really good stuff. I tell people to do that as well. When you are in an interview and you've done a project, point back to those projects. You know, even if you've never used them in a job, if they ask, you know, how have you ever done data cleaning and you've never done data cleaning in a job, you've done it in a portfolio project, you can say, I've done it in a project here, the methods that I used, and that is a much more compelling case. And you're able to talk more specifically about the skill itself and how you did it. Some long transitions, man. The third thing I recommend is to look for opportunities to practice data science in your current job. Although you're an analyst, you still have access to data. It never hurt to go a little above and beyond to experiment with some more advanced algorithms. No, that's a great, that's a great thing. I'm stopping at completely random places, but I want to say that that is a very true thing. I've done this myself, you know, when I get into a role, the first thing I do is I see how they do things, how they use the data, what they're using to clean it, process it, etc. And then I try to figure out ways to improve that. And so I think that's a really good thing to do. If you're in a role where you're already working with data, you know, pitch that to your boss, see if they are willing to let you try some new things on it. Most of the time, if you make a compelling argument for what you're trying to do and it actually helps the business in some way, they're usually pretty willing to let you do that. It might take a little extra time, but you should definitely try to share these things with your manager or with data science teams. That's assuming you're doing a great job with your regular work as well. He's doing some really weird transitions and background stuff in this video. It's been really funny. I don't know why he's doing it, but maybe something I need to start doing. I'm not sure. Let your job pay to upskill you. That almost never happens. Let's see what he has to say. Now, moving back to data analyst to data scientist, I personally think continued education in the form of certificates or university programs is a great way to learn data science concepts. But I don't have much faith in them helping you to land a job. The exception would maybe be a master's degree, but these can be very expensive and time consuming. And I've made plenty of other resources on if you should pursue that route or not. I don't think it's for most people, but it could be useful to some. If you feel the bottleneck for you to go from data analyst to data scientist is knowledge, I highly recommend getting educated on your company's dime. All right. So here's my problem with this. I feel like most employers don't actually do this. It would be amazing if employers were super willing to invest in you and have you upskill to become a data scientist. But in all honesty, I feel like that just doesn't happen or doesn't happen often. It would be fantastic if my employer was like, Hey, we want to pay for you to get a master's in computer science. But that just doesn't really happen that often. Most companies have internal job boards that give you the option to apply for a new role even before the position opens to the public. The overhead of hiring someone who's already employed at a company is a lot lower. So this can really work in your favor. I highly recommend that you learn about resources and the opportunities within your current company. I think there is a lot of truth to this. In fact, I think you have a significantly higher chance of landing a data scientist role in your current company than at an outside company. When you have the title of a data analyst and you try to transition into a data scientist role at a different company, it is a lot more difficult because they do not know you. They don't know the quality of work that you do, but your current company does and they see all the hard work that you're doing. And they see the rapport that you have with the managers and the employees and they see your coding ability. And so it makes it a lot easier when you already have that personal touch or that kind of in with that company. If you've tried all the things that I mentioned earlier, and I mean like really tried them, then it probably makes sense for you to look outside of your current company. To do that, I recommend leveraging your existing- I'm guessing this is mom. You have connections to friends. Grandma. Your university through your various interest groups. She seems sweet though. And there's plenty of great resources to join online. If you feel like you don't have a good network, I think you should go to meetups when it's safe again or find communities online that you can engage with. You'd be shocked at how many opportunities you find when you're talking with or when you're just around like-minded people. These communities are also an incredible place to develop your skills further. There's a double benefit. Again, I agree and disagree on this one. I agree that joining communities is a fantastic place to learn. I have an Alexi analyst discord. I'll leave a link in the description if you want to join that. Where people talk about SQL and Python and getting jobs and interviews and all these things. And it's a really good place to learn. I just don't think that these types of communities actually help you land a job. And I've been parts of tons of communities and forms in the past. And I've never had that experience or seen that with anybody else. But that's just my experience. You know, maybe the 66 days of data community really does help you find a job. That just hasn't been my experience with all the ones that I have been in the past. But I do think it's a really good thing to do. I think you learn a lot of new concepts. You learn a lot of things that you maybe didn't know before. And it helps you prepare for when you actually are ready to make that transition. Enjoyed this video. I also hope that it helps you to break into the data science field. Again, if you haven't checked out the podcast, Ken's nearest neighbors, please do. And until next time, thank you for watching and good luck on your data science journey. So overall, I think Ken had a lot of really good tips to make that transition from data analyst to data scientist. I especially liked the portfolio. I think that is super important. And I really liked the tip on looking internally first. I think that is really, really important to do and probably one of the better tips from that entire thing, in my opinion, some things I didn't agree with. I don't think that most employers are going to be paying you or buying certificates or paying for you to take certificates. I just don't see that happening that often. If you are in a company that actually offers that, please take advantage because there are a lot of people that are very jealous of you. So overall, I like the video. I like the tips. I like the funny intro. I like the transitions. I really had no beef with this at all. I don't really know what you're referencing. And honestly, Ken, if we're having problems, I wish that you come to me one on one instead of making these public posts. You have my number. Give me a call. We'll handle any conflict, any disagreement, but all that behind us. We'll just talk about it like brothers. With that being said, if you want to watch Ken's original video, I will post a link in the description. Thank you guys so much for watching. I really appreciate it. If you liked this video, be sure to like and subscribe below. And I'll see you in the next video.