149: I Asked DeepSeek How to Become a Data Analyst (It scared me)

If you haven't heard of deep seek yet, just wait. It is a new AI platform that supposedly is maybe even better than open AI and chat GPT reasons being number one is currently free and open source, meaning it is a lot more easily accessible to the entire world. And number two, it has some pretty cool thinking properties where it actually shows you it's. Thought process, and you can see how the computer algorithm and the AI is actually thinking through the problem. So I thought, why not ask DeepSeek on how to become a data analyst and see what it tells me. So let's go. So I'm on DeepSeek's website and I'm going to turn on the DeepThink R1 model, which basically is their better reasoning model right here. And I'm going to ask, I want to be a data analyst. What should I do? And hit enter. One thing about DeepSeek is it is a lot slower because it is actually going through and thinking through your process step by step. So although OpenAI and ChatGPT may be faster, DeepSeek is actually taking its time to think through the problem. And you see those thoughts right here. So it says, okay, the user wants to become a data analyst. Let me start breaking down what that entails. First, I need to outline the essential skills they need. Data analysis requires technical skills like SQL, Excel, maybe Python or R. I already love that the maybe is here. If you guys have watched any of my videos or listened to any of my podcast episodes, you know that I am not a fan of learning Python. First, and I think SQL and Excel are a better place to start. Visualization tools like Tableau or Power BI are important too. Yes, they should also understand statistics and data cleaning. Next education. Do they need a degree, a background in math stats or CS helps, but it's not mandatory. So far, I really agree with what DeepSake is saying. Obviously it helps to have those. STEM background, some sort of math or stats or computer science, but it's not mandatory. Maybe mention online courses and certificates as alternatives. Platforms like Coursera, edX, Datacamp come to mind. Okay. Certifications from Google or Microsoft could also add credibility. I'm going to be honest. I don't think either of those add a ton of credibility. The Google and the Microsoft certificate certificates still don't mean anything in the data analytics world, but they don't hurt your credibility. So I guess that's fine. Projects are crucial for experience. Oh my gosh. I agree with this. That's part of the SPN method, right? In order to land the data job, you have to have the skills. You have to build projects and you have to have a network. So projects are crucial for experiences. They should work on real world data sets to build a portfolio. I totally agree with that. Where can they find data sets? Kaggle, government portals, maybe APIs, suggest projects like sales analysis or COVID trends. Totally. We do sales and marketing analysis in the accelerator program. Uh, and we do some healthcare analytics. We don't do COVID because COVID has been done a bajillion times, but we do actually diabetes building a GitHub portfolio and personal website would showcase their work. Uh, yes, I would reverse those where I would do a personal website first versus a GitHub portfolio. If you're just trying to land the data analyst job, if you're going for a data scientist or data engineer, that's a little bit different, but I liked that it didn't just say Gab. I liked that it said personal website, networking and job search strategies. Yes. That's the end part of the SPN method. I like DeepSeek so far. LinkedIn is important, it sure is, as well as attending meetups or conferences, tailoring resumes to highlight relevant skills and projects, applying to entry level positions like junior data analyst or business analyst, maybe internships if they're just starting out. Continuous learning is key in this field. That is so true. Encourage joining communities, following blogs, podcasts. Hey, shout out to, uh, podcasts. If you want to follow a podcast, check out the Data Career Podcast. Staying updated with trends like AI and machine learning. Also, soft skills like communication and problem solving are often overlooked, but vital. So far, so good. I really agree with what DeepSeek is saying. Wait, did I miss anything? Okay. It's so weird to have. A, an AI model say something like that. Oh, maybe the tools in more detail. Excel for basic SQL for databases. Python slash R for advanced analysis. Visualization tools like Tableau, Power BI, maybe even Looker. Cloud platforms are becoming more relevant too, like AWS and Google Cloud. Also the importance of understanding the industry they want to work in. Man, 100 percent I agree with that. Domain knowledge can set them apart. Yes, it can. Like if you're trying to break into data, whatever previous experience, whatever previous job, whatever previous education you've had, that's, what's like going to be your in, into the world because data analysis is in every industry. So it's like, that is where you need to focus on. Uh, so I liked that. I got that. Domain knowledge can set them apart. Maybe healthcare, finance, e commerce, et cetera. Tailoring projects to specific industries might help. Once again, a hundred percent correct. This is why in the accelerator. We do nine different projects in nine different industries. So no matter what industry you're interested in, we have a project that you can pitch to a hiring manager recruiter. Hey, look, I have, I've done something very similar to what your job requirement is asking you to do, asking me to do. Here's my evidence that I can do this. Should I mention freelancing or volunteer work as a way to get experience that could be useful for building a resume, maybe suggest platforms like Upwork or volunteering for nonprofits. I love the volunteering for nonprofits. I think that's great. I think most people do. Who try to go the freelancing route. It's pretty difficult because freelancing is basically, you have to be good at whatever you're doing. So in this case, data analysis, but you have to be good at data as well. I mean, you're just starting out to be perfectly Frank, unless you have freelancing experience some other way, you're bad at both. So it's not my favorite place. To start if I was you, but it didn't, it just said, maybe we can consider them. What about interview preparation? They might need to practice SQL queries, case studies, and presenting their projects, behavioral interviews as well. Resources like sleek code for SQL practice or mock interviews. Let me structure this step by step start with learning technical skills, then education and certificates, building projects, networking, job applications, and continuous learning. Make sure each section is clear and actionable avoid jargon. So it's accessible. Maybe include resources and examples for each step. Becoming data analyst involves building technical skills, getting practical experience and showcasing your abilities. Here's a step by step guide to help you get started. All right. Just like the thinking was very good. And obviously that's not something you get with open AI and chat GPT. So I loved hearing how it was thinking. And that way I can kind of interpret the context of the results with what it was thinking. Uh, let's go ahead and take a look at what it officially suggests. Build foundational skills, technical skills, Excel, master pivot tables, VLOOKUP and data visualization. That is super simple. I like that a lot. SQL. Learn to query databases. Join aggregations. Resources. Mode. Analytics. SQL tutorial. Kaggle. Learn SQL. Both two great resources for learning SQL. Python slash R. Focus on libraries like pandas, NumPy, and visualization tools. Resources. Data camp. Free code camp. NumPy. I mean, it's a, it's fine to learn pandas is the big one in visualization tools. NumPy actually pandas is built on NumPy. So by knowing pandas, you kind of can do a lot of the stuff that NumPy can. There are some things that you need to use NumPy for alone, but there's not that many data visualization, learn tools like Tableau, Power BI or Looker Tableau, public Tableau training. Great. Statistics, grasp basics like hypothesis, testing distributions, and regression Khan Academy, StatQuest YouTube channel. Shout out StatQuest and Josh Starmer. I've done a video with Josh in the past. Uh, we'll have that in the show notes down below. I also love that it did statistics last out of all of these, because often people are like, don't you have to know statistics to become data analyst and you do, but it's maybe less important than you realize. For instance, you can do a VLOOKUP really without doing any sort of. Tons of statistics. There are aggregations there like sums and averages, mins and maxes, but the chances are you've probably learned that in high school or college at some point. So something more complicated like hypothesis testing or regression, those are important too, but not as important as knowing how to do a VLOOKUP. I love that it's including soft skills here too. Soft skills, storytelling, present insights clearly to non technical audiences. Problem solving breakdown, complex questions into data driven solutions. A lot of the times, and honestly, like if you go look at the platforms, it suggests like data camp and free code camp, they really miss on the storytelling. And it's really important because we had cool math, like on the podcast recently, and she's like, people don't take you seriously. If you're unable to communicate your story and your work. Effectively. So it's often overlooked. One of the things we do inside the accelerator is one, we post our stories on LinkedIn in our about section. And we try to post on LinkedIn often, but also we post our projects on there with a written writeup. Um, and that written form of communication, we actually will teach you how to do video communication as well. Like if you like recorded yourself with a Google slide presentation or with your dashboard, but those types of. Communication and storytelling opportunities are really what can set you apart as an aspiring data analyst. Next section, formal education and certificates. So degrees optional. Love that it says optional. That is very friendly. I appreciate that. I'd agree in math, statistics, CS, or economics is helpful, but not mandatory. I agree. Online certificates, Google data analytics certificate, and the Microsoft data analyst associate power BI plus Excel and the IBM data analyst professional certificate. Obviously those are all good and fine. Gain practical experience. I love that it's saying this. This is awesome. Projects, analyze data from Kaggle UCI learning machine learning repository or government portals, data. gov. I think that is a hundred percent correct. Example projects, sales trend analysis, COVID 19 impact dashboard, customer segmentation, obviously not a whole lot of like, this is just an intro. Right. So it's not really giving us any more information on these, but that's great. Portfolio share code on GitHub and create visualizations in Tableau public. Build a personal website using GitHub pages or WordPress to showcase projects. Love that it got GitHub pages. That is something that a lot of people miss. Now I need to make a different video on this, but GitHub pages is very separate from GitHub. It is like it's from GitHub, but it's like a separate product. It's free, but it's basically like the ability to build personal websites. And I love that it got it. I personally recommend card now. We'll have a link in the show notes. To to check out card thing is the best and easiest place to start building your portfolio. Freelance slash volunteer offer services on Upwork or nonprofits. I like helping nonprofits more because I think they could offer more support and like a more formal role. Once again, I think freelancing on Upwork, especially if you've never freelanced before, it's not going to lead very far because. Freelancing requires a ton of business experience. You have to know how to market yourself. You have to know how to ask a lot of questions. There's no one checking your work. So I would lean on the volunteer side versus the freelance, but I don't mind them mentioning it. Okay. Number four, network and apply for jobs. LinkedIn optimize your profile with keywords like data analysts and connect with professionals. You guys, I can't tell you how important this first line is. And it really, if you just read it, you're like, okay, that makes sense. What does that actually mean? You guys, this is one thing we talk about in the accelerator. The more you put the term data analyst on your LinkedIn profile and your resume, the better you'll be off. ATS is the LinkedIn recruiting algorithm is dumb. One of the ways it actually like checks to see how relevant you are to, for instance, if you're applying to a data analyst role is how many times do they have the word data analyst on their. LinkedIn page. And that phrase can be anywhere that could be in your headline that can be in your about section that can be in your experience section that can be in your education section. For instance, if you just put aspiring data analyst in your experience section, that actually almost works as good to a computer as putting the term data analyst. So that is really key job platforms, entry level roles, junior data analyst, business analyst, reporting analyst. Those are all goods search on LinkedIn indeed, or specialized sites like well found. Yeah. Well found angels lists. I'm a fan of, but not really for entry level roles. They're more senior roles there. Instead, I would try something like findadatajob. com or premiumdatajobs. com. Those are two job boards that I run where we try to be more entry level friendly. Meetups, attend events, data science meetup, Pi data or virtual webinars. I think that's great. That's a form of networking and obviously a great option. Okay. Number five, ACE interviews, technical prep, practice SQL on leak code or hacker rank review, statistical concepts and case studies, behavioral questions. Use the star method to answer questions about teamwork and problem solving and portfolio walkthrough. Be ready to explain your projects, goals, process, and impact. Once again. This feels really good because most people are all about the technical prep and the technical prep is important, but I would say, honestly, at least half of my students who land jobs through the accelerator program, never really even have a formal technical interview. The other 50 percent definitely do. And it's good to be prepared using things like leak code or hacker. I prefer things like strata scratch, data lemur or analyst builder. Instead of these, they're just more data oriented instead of like. Computer science and stuff like that, I think, but I just want to point off that most people ignore behavioral questions. And that's one of the things I try not to ignore with interview simulator. If you guys go to interview simulator. io, this is my interview platform where you can practice your behavioral questions. And then I love that it has the portfolio walkthrough as well and being able to talk about your projects because really, if you can get an interview and you can say, Hey, I have this portfolio. I've done this project that's similar to what I would be doing on the job. I think that is an opportunity for you to. Try to take the interview kind of by the reins and flip it on them. And they ask you questions about your project versus just like asking random statistical concepts. So that's going to make you feel more comfortable and make you look better as well. Number six, keep learning, stay updated, follow blogs like towards data science and podcasts like data skeptic. Those are both great. I would add data career podcast to the podcast, but if you're listening to this, you're probably already following our podcast, advanced skills, explore machine learning, scikit learn, cloud tools, AWS. Google Big Query or A B testing. I think those are, I mean, that's fine. You're always going to be learning in this world, but it didn't really talk about job applications and applying like you don't want to just like go to advanced skills without job hunting a ton because you can get paid to learn machine learning and cloud tools and A B testing on the job. Most entry level roles, even maybe middle roles don't even require that. Uh, example, learning path, Excel, SQL, Tableau month, one to two months, three to four Python and statistics and month five to six build three or four portfolio projects and apply for jobs. Now, if you've listened to any of my episodes previously, you know, that I think that most people, um, if they're willing to put in, you know, 10 to 20 hours a week can cut this in half. And there's like certain things that we can do where it's like, we're not going to spend an entire month learning statistics, an entire month learning Python. You just don't need to, when you're landing your first day at a job. And also like why wait till month five and six to build your portfolio projects and apply for jobs. In the accelerator, you'll have a project built within your first 10 days, your first project built 10 days guaranteed. Like if you just put it in the hours, like done, we'll have your first project. Build and then we want to start applying for jobs, you know, well, before we hit the six month mark, we're probably talking to the two month mark, if I'm being honest, because applying for jobs isn't art and you'll get better at it as you go on. But overall, I don't hate this plan at all by combining skills, projects, and networking. Holy crap. Did they steal that from me? Skills, projects, and networking. That's the SPN method. I came up with the SPN method. I'm the only one who's ever put those things right next to each other. Skills, projects, and networking. Call it the SPN method. I built it myself. I have a notebook somewhere over here where like, I just, I wrote down my whole like framework and like tried to figure out what to call it. And we ended up landing on SPN. Wow. You'll position yourself strongly for a data analyst role. Stay smart, stay consistent and iterate based on feedback. Overall, you guys, like, I feel like this was a pretty good roadmap. Right here. Like I'm pretty impressed by this and, uh, it's not the worst thing on planet earth. Like it, it did a better job almost in the instructions. I think of like the Python where it was like maybe Python and R somewhere up here. Right. And, but I think it did great on mentioning the behavioral questions. I think it did really good on the networking and the, and the projects and the portfolio. I thought I did great talking about GitHub pages overall. I think if you followed this plan, you would be. Pretty well off. I mean, this plan is basically what I outlined in my previous episodes. It's basically following the SPN method. I mean, literally it says by combining skills, projects, and networking, you'll position yourself strongly for a data analyst role. And I agree like that, the SPN method will set you up exactly. This way. So, uh, I really like this from deep seek. I'm going to play around with this more. If you guys want to follow the SPN method, please consider joining the accelerator program. This is basically a coaching led and group cohort learning style where you're basically going to do all of these things, but we're going to give it to you exactly step by step. You're not going to have to go figure out like, you know, how do I learn data visualization and Tableau public? Or like what courses should I take? We'll give you the exact roadmap. We'll teach you the exact projects. We'll give you the exact data to build your projects, to learn the skills and to grow your network. We'll show you exactly how to actually optimize. Like what does it actually mean to optimize your profile with keywords like data analyst? So that's of interest to you. We'll have a link in the show notes down below and let me know what you guys want me to do next with deep seek down in the comments. Should I try to analyze data? Should we compare it to something like chat GPT? Let me know in the comments down below.