In this 30‑minute YouTube monologue, data‑career coach Kedisha Bryan walks viewers through a self‑assessment designed to reveal whether they are naturally inclined toward a data analyst, data scientist, or data engineer role. She begins by warning that choosing the wrong data path can waste years, money, and health, then introduces a six‑question test where each answer maps to a specific role. For each question she explains the mindset, typical tasks, preferred tools, and work‑environment preferences associated with the three roles, and how the answer indicates a fit. After the test, Kedisha shows how to tally results, interprets dominant role outcomes, and provides detailed salary ranges, typical skill‑sets, and career progression for analysts, scientists, and engineers. She also outlines hybrid roles (BI manager, analytics engineer, ML engineer) for viewers who score high in multiple categories, describing their responsibilities and compensation. Throughout, she promotes a free training roadmap and paid mentorship to help viewers acquire the necessary skills and land a data job within six months, urging them to click the description link and watch the next video for the full roadmap.
Chapters
Choosing the wrong data career path will cost you years of your life. It will cost you your financial future and can destroy your health and wellbeing. But I'm not gonna let that happen to you. That's why I built the ultimate data career test. For each one, you pick the right answer that your instinct tells you to. Count how many you get, and remember, there is no wrong answer. The result will tell you the true path that you should be taking. My name is Kedisha. I'm a data career coach who went from delivering pizzas for $8 an hour to becoming a self-taught data scientist. And I have helped thousands of other career changers become data analysts as well. I've watched people choose the wrong path and struggle to land a role. I've also watched people land their perfect fit and skyrocket to 6 figures in under 2 years. The difference? They knew which role matched their natural strengths. Ready to find yours? All right. Question number 1. You just landed on a new project and your stakeholder says, we need to understand our customer behavior better. What's your first instinct? A, I wanna build a predictive model. Let me identify patterns and forecast what customers will do next. I want to map out the data infrastructure. Where is this data even coming from and how was it even being collected? Can we automate the process and how can we even trust that it's correct? I want to explore the data visually. Let me create some charts to see what stories are hiding behind the numbers. Got your answer? Good. Now listen closely. If you chose A, you think like a data scientist. You're not satisfied with just understanding what happened. You want to predict what's coming next. Data scientists build models, run experiments, and use statistics and machine learning to forecast outcomes. If you love math algorithms and the idea of building something that can predict the future, this is your path. If you chose option B, you think like a data engineer. You're not just jumping straight into analysis. You wanna understand the system first, and you don't inherently trust the data. Engineers build the pipelines, infrastructure, and the systems that make sure the data flows cleanly and efficiently. If you get frustrated by inefficient processes and love the idea of building automated solutions that scale, you're already a data engineer at heart. If you chose option C, you think like a data analyst. You're drawn to clarity. You wanna take data and turn it into something people can understand and act on. Analysts are the storytellers. They don't just crunch numbers, they translate data into actual decisions. If you get excited about dashboards, presentations, and helping stakeholders see the aha moment, this is your lane. So tally your score, 1 point for whatever letter you choose, and let's keep going. Okay, question number 2. You're in a meeting with executives and they're asking about last quarter's performance. Which role do you wanna play in the room? A, I don't wanna be in the meeting at all. I wanna be the one who built the system that automatically delivers that data to them every week without anyone else lifting a finger. I wanna present the insights, show them the dashboard and walk them through the trends and recommend what they should do next. I wanna show them the predictive model. Here's what's likely to happen next if we don't change course, and here's the probability of success if we do. All right, got your answer? If you chose A, that's the data engineer. Engineers are the behind-the-scenes heroes. They don't want to present, they want to build systems so good that presentations run themselves. If you rather automate the entire reporting process than sit in another meeting, you're already thinking like an engineer. If you chose B, that's the data analyst. Analysts are the communicators. They're comfortable in front of stakeholders and they know how to take complex data and make it digestible. If you like the idea of being the bridge between data and decision makers, and if you enjoy presenting and influencing strategy, you're an analyst. If you chose option C, that's the data scientist. Scientists bring the what-if scenarios. They're the ones running simulations, testing hypotheses, and showing leadership what the future could look like based on their data. If you wanna be the person who brings predictive power to the table, this is you. Okay, tally your score and let's move on. Question number 3, you're learning a new tool. How do you approach it? A, I wanna understand how it works under the hood. What's the architecture? How does it scale? Can I automate it? B, I wanna run experiments, test different models, see what works, and optimize for accuracy. C, I wanna build something visual, a dashboard, a chart, Something that I can show people and explain. Got it? Let's break it down. If you chose A, you're thinking like a data engineer. Engineers are the architects. They want to understand a system, the infrastructure, and the scalability. If you care more about how something is built than what it produces, you're an engineer. If you chose B, you're thinking like a data scientist. Scientists are the experimenters. They want to test, iterate, and optimize. And if you love the scientific method and the idea of improving accuracy through trial and error, you're thinking like a scientist. If you chose C, you're thinking like an analyst. Analysts. Analysts are the visual learners. They want to be the output, understand the story and communicate it. If your first instinct is to build something you want to present, you're already thinking like an analyst. Add to your tally and let's keep going. Question number 4: Sales are down 20% this quarter. What's your first move? Option A: Build a model. I want to identify the variables that predict sales drops and forecast what's going to happen in the next quarter. Option B: Dig into the data. Which products? Which regions? Which customer segments? I want to find the story and present it. Option C, check the data pipeline. Is the data even accurate? Are we capturing everything we need? I want to make sure the system is working correctly first. All right, let's break it down. If you chose A, that's the data scientist approach. Scientists want to predict and prevent. They're not just looking at what happened, they're building models to forecast what's coming next. If you want to be the person who sees what comes around the corners, you are the scientist. If you chose B, that's the data analyst approach. Analysts are investigators. They dig into the details, They find the patterns and communicate the insights. If your instinct is to explore the data to tell the story, you are thinking like an analyst. Option C—that's the data engineer approach. Engineers are skeptics at heart. They don't trust the data until they verify the source. If your first instinct was to check the infrastructure and to make sure the data is reliable, you're already thinking like an engineer. Tally your score. We're halfway through. You're watching this and thinking, okay, I'm starting to see which path fits me, but I have no idea how to actually break into this field. I got you. I create a free training that walks you through the exact roadmap that my my students and community of over 80,000 use to land data roles in 6 months or less without going back to school and wasting money on boot camps and courses that just don't work. Click the link in the description to access that training. If you're ever ready to work with me directly, we can talk about how to fast-track your transition with real mentorship, real projects, and real results. All right then, let's get back to the test. Question number 5, which tool excites you the most? A, SQL, Airflow, Spark. I want to build data pipelines and automate workflows. B, Tableau, Power BI, and PowerPoint. I want to build dashboards and presentations that tell stories and drive decisions. Or option C, Python or R. I wanna build models, run statistical analysis, and predict outcomes. Got your answer? Let's talk about it. If you chose option A, you're the data engineer. Engineers love infrastructure tools because they're all about efficiency and scale. You don't wanna manually pull data every week. You wanna build a pipeline that does it automatically, and you want systems that can handle millions of records without breaking a sweat. If you get frustrated by manual processes and dream about building automated workflows, you are an engineer. If you chose B, you're a data analyst through and through. Analysts love visualization tools because they're all about making data accessible. You're not about just building pretty charts. You want to build clarity. You're the person who can take a spreadsheet full of numbers and turn it into a dashboard or presentation that makes a VP say, now I understand. If you light up at the idea of designing something that helps people make better decisions this is your calling. Option C. Okay, if you chose C, you're a data scientist. Scientists love programming languages because they give you power. Python or R lets you manipulate data in ways that Excel never could imagine. You get to build predictive models, run A/B tests, and create algorithms that learn from data. If you get excited about writing code that can forecast customer behavior or optimize pricing strategies, you are a scientist at heart. Now here's the thing that people don't realize: the tool that you're drawn to reveals how you think about problems. Analysts like to think about communication. Scientists think prediction and engineers think about automation. So add to your tally and let's keep going. All right, question number 6. What kind of work environment do you thrive in? A, independent. I wanna work on complex problems, run experiments, and optimize models without constant interruptions. Option B, behind the scenes. I wanna build systems that work so well nobody has to think about 'em. Option C, collaborative. I wanna work with stakeholders, present insights, and influence decisions. All right, now let's break it down because this question reveals something critical about your personality. If you chose option A, you are a data scientist. Scientists are the deep thinkers. They thrive in environments where they can focus on complex problems without the constant interruptions. You need time to experiment, to test hypotheses, and to optimize your models. And you're not antisocial per se, but you just need some space to think. If you get frustrated by back-to-back meetings, would rather spend your day coding and analyzing, you're a scientist. If you chose B, you are a data engineer. Engineers are the builders. They thrive in environments where they can create infrastructure that scales. You don't need recognition. You need to know that your systems are running smoothly. You get satisfied knowing that you built something so reliable that nobody has to worry about it. If you'd rather build a system that eliminates problems than sit in meetings talking about the problems, you're an engineer. If you chose C, you're a data analyst. Analysts are collaborators. They thrive in environments where they can communicate and influence. You're the person who actually enjoys stakeholder meetings because you get to see your insights drive action. You wanna be in the room where decisions are made, and you wanna present your findings and watch people's faces light up when they understand the data. If you get energy from working with people and influencing strategy, you're an analyst. Now here's what I want you to understand. There is no right or wrong answer. Some people think analysts are less technical because they work with stakeholders. That's garbage. Communication is a skill. Influence is a skill, and companies pay top dollar for people who can do both. So tally your score and let's move on. Okay. Okay, last question. Which skill do you want to master ultimately? A, data architecture. I want to design systems that scale millions of records. B, machine learning. I want to build models that predict the future. C, data storytelling. I want to communicate insights so clearly anyone can understand them. Got your answer? Let's talk about what this means. If you chose A, you are a data engineer. Data architecture is the foundation of every data-driven organization. Without good architecture, nothing else works. Dashboards breaks, models fails, reports are inaccurate. If you want to master this skill, you are going to be the person that makes everyone else's job possible, and companies know it. And that's why engineers are some of the highest paid professionals in tech. If you chose B, you are a data scientist. Machine learning is one of the most in-demand skills in tech right now, but here's the thing: it's also one of the hardest to master. You need to understand statistics, algorithms, programming, and business context. If you're willing to put in the work to master this skill, you're going to be incredibly valuable because companies are willing to pay top dollar for people who can build models predict the future accurately. If you chose C, you are thinking like an analyst. Data storytelling is one of the most underrated skills in data and one of the most highly sought after. Most people know how to run a query, most people can build a chart, but very few know how to take complex data and communicate it so clearly that a non-technical person can understand it and act on it. If you want to master this skill, you are going to be in the top 1% of analysts because companies are desperate for people who know how to bridge the gap between data and decision-making. Here's the bottom line: the skill you want to master reveals your long-term potential. So So choose wisely. All right, now tally your final score and let's reveal your results. Count up your A, Bs, and Cs, and this is where things get interesting. Your answers aren't in the same order every time, so you need to count based on the roles, not the letters. Go back through your notes and count how many times you got data analyst, how many times you got data scientist, and how many times you got data engineer. If you got mostly data analyst answers, your path is clear. You are a communicator, a data storyteller, and you are a strategic partner at heart. You thrive when you know you can take data and turn it into insights that drive decisions. Focus on mastering SQL, Excel, a BI tool like Tableau or Power BI, and then PowerPoint, then storytelling as well. This is the fastest path to 6 figures for most career changers. Entry-level data analysts can start around $60K to $85K, but as you gain experience, become a senior analyst, you'll be looking at an average of about $105K or more. If you have mostly data scientist answers, you are a problem solver, an experimenter, and a technical expert. You thrive when you can build models to predict outcomes. Focus on mastering Python, statistics, machine learning, A/B testing, and experimentation. This path requires much more technical depth and not very easy for career changers to pivot into, but the payoff is there. Even entry-level data scientists can land jobs at $95K to $130K range, and with experience, senior data scientists can regularly earn well over $190K. If you had mostly data engineer answers, you are a builder, an architect, and a systems thinker. You thrive when you can create infrastructure that scales, so focus on mastering SQL, Python, cloud platforms like AWS or Azure, and data pipelines. This is one of the highest paying paths in data and the demand is exploding. A starting data engineer can expect to make between $85K to $125K. And as you become a senior data engineer, that average jumps over to $155K. But here is the twist. What if you are tied and what if you scored high in 2 categories? If you scored high in analyst and scientist, you would be perfect for a BI manager or an analytics lead. Leadership roles typically range from $115K and can go all the way up to $190K as you gain more experience. If you scored high in analyst and engineer, you are a perfect fit for an analytics engineer or a business intelligence engineer. You can tell stories and build systems. Companies are desperate for this hybrid skill set. The analytics engineer is a newer, higher demand role with salaries that starts around $80K and quickly growing over $125K with just a few years of experience. Now, if you scored high in scientist and engineer, you would be a perfect fit for an ML engineer or a machine learning engineer. You could build models and deploy them at scale. This is one of the most in-demand roles in tech right now. Entry-level ML engineers can see total compensation packages of at least 6 figures, as high as even $200K. Okay, so there you have it. Now you know your true data career path, but knowing your path is only half the battle. The other half is actually building the skills of landing that job. And to learn the exact roadmap to launch your data career, this next video will just be for you.
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