Don't Become a Data Scientist (Do THIS Instead)

You've been chasing the wrong 6-figure dream. Right now, thousands of people are grinding through linear algebra courses, spending months trying to learn neural networks, and drowning in machine learning theory, thinking that's their ticket to a high-earning data career. Meanwhile, there's this other role, one that pays just as much, sometimes even more, that's hiring like crazy right now. And the best part, you don't need a PhD, you don't need years of advanced math, and you definitely don't need to spend the next 2 years of your life studying theory that you'll never actually use. And here's the kicker. Most people have no idea that this role even exists. They're so busy chasing that shiny data scientist title that they're completely missing the opportunity that's right in front of them. And by the end of this video, I'm gonna show you exactly what that role is, why it's actually the smarter move for most people, and the 4 specific skills you need to master to land one of these positions. But first, let me tell you why the data scientist path is actually a trap for career changers. Now, if this is your first time here, I'm Kedisha, and 5 years ago I was delivering pizzas and Amazon packages for $8 an hour, and today I'm I'm running a community of over 80,000 current and aspiring data professionals, and I've helped thousands of other career changers break into data analytics without going back to school and wasting time and money on courses that just don't work. Okay, now let's talk about the elephant in the room. Data science has been sold to you as a successful, high-paying career that anyone can break into. You've seen the headlines: sexiest job of the 21st century, learn data science in 6 months, No degree required. But here's the truth that no one tells you. That's complete garbage. Look, I'm not saying data science isn't a real career. It absolutely is. But for the average career changer, it is one of the worst paths you can actually take, and I can prove it. First, let's talk about what data science actually requires. Real data scientist roles, the ones that companies actually pay well, they're looking for people with PhDs or at minimum a master's degree in a quantitative field. We're talking about coursework in vector calculus, linear algebra, advanced statistical inference, Bayesian statistics, optimization theory. The list goes on and on. This isn't stuff you just pick up in a 6-week boot camp. This is years of graduate-level academic work. I've talked to people who've gone through master's degree programs with data science, and you know what they tell me? They were shocked at how math-heavy it really was. One person told me they spent an entire semester just on probability theory. Another said they had to take 3 separate courses on different types of calculus before they can even touch machine learning. And here's the thing, if you're coming from a non-quantitative background, if you weren't a math major in college, and if you're just learning these new skills, you're essentially going to be going back to school for to build a foundation you don't even have. But let's say you're willing to do that. Let's say you're willing to go to school and spend 2 years getting that master's degree. You graduate, you're excited, and you start applying for data scientist roles. And you know what? You will see entry-level data scientists, 5+ years of experience required. I'm not making this up. Go to LinkedIn right now and search entry-level data scientist positions. You'll see posting after posting demanding 5, sometimes even 7 years of experience of so-called entry-level roles. I was talking to someone a few weeks ago who has over 5 years of data scientists in their title, and they've been applying for months. Over 300 applications. And you know how many interviews they've gotten? 3 interviews out of 300 applications. The entry-level data scientist job market isn't just competitive, it is a ghost town. The jobs don't exist, and the very few that do are being taken up by experienced professionals who are downleveling themselves because they can't find a senior role. Now here's where things get painful. While you're spending 2 years learning linear algebra and machine learning theory, preparing for a job market that doesn't even exist anymore, there are other people with less education than you High-paying analyst roles right now. Think about that. You can spend the next two years in grad school, go into twenty K plus into debt, and then struggle to land an entry-level data scientist position. Or you can spend the next six to even nine months learning practical business-focused skills and land a role that pays just as much, if not more. It's like training for a marathon by buying a yacht. You're preparing for the wrong thing entirely. And here's what really sealed the deal for me.

LinkedIn just laid off nearly seven hundred employees. Qualcomm is planning to cut more than twelve hundred jobs. Google, Amazon, and Snap are among the companies continuing to downsize. Units affected, they also include hardware engineering, ad sales.

In 2023 and 2024, we saw massive tech layoffs just like what's happening now. Companies cut their data science teams, but at the same time, they were scrambling to build AI capabilities and they quickly ran into a problem. Their AI models were only as good as the data feeding them. Garbage in, garbage out. So what did they do? They started hiring like crazy for roles that could build clean, reliable data infrastructure, not theorists who can just design models. Builders who could supply those models with quality data. The market is telling you exactly what it wants. The question is, are you listening? So if data science is a dead end for most of us career changers, what should we be doing instead? Let me introduce you to a role that's actually hiring right now, the role that pays data scientists money without the academic barriers, and the role that's literally designed for career changers. It goes by a few different names: data analyst, analytics engineer, business intelligence engineer. It's all the same concept, and it's a secret that people landing high-paying roles already know. Here's a simple version: an analytics bridges the gap between pure data engineering and pure business analysis. You're not just analyzing data, you're also building some small pipelines. You are there for the entire end-to-end process. Let me paint a picture of what that looks like. A data engineer will build the raw pipeline that pulls data from different sources. That's their job. They get the data into the warehouse. Then you, the analytics engineer, you step in and you clean that data, transform it, test it, and document your process. You apply simple best practices to make sure it's highly reliable and highly reusable, and you turn that raw data into clean data products. You can build build dashboards, reports, and presentations to business stakeholders that actually make decisions. You are the architect of the entire data story from messy raw data all the way to executive presentations. And here's why companies are willing to pay big money for this role. You make everyone else more productive. Think about it. Data scientists need clean, reliable data to build their models. You provide that. Business analysts need trusty metrics to make recommendations. You provide that. And executives need dashboards that they can actually understand, and you provide that as well. You're not a cost center like other jobs. You are a force multiplier. I was looking at a job posting the other day from Apple and DoorDash last week. You know what they're hiring analytics engineers to do? Build and maintain analytical data pipelines and deliver trusted data and dashboards to support decision-making. They're not just looking for theoretical knowledge. They are asking for a tangible impact. Now let's talk about what really matters, the salary. According to a 2024 salary report, the median analytics engineer salary is $178,000. Let me say that again, $178,000. You're making data scientist-level money, but you're getting there on a skills-based path, not a credential-based path. No PhD required, no 5 years of experience required, just the right skills and the ability to prove that you can deliver. All right, so you're convinced you want to become a full-stack data analyst or analytics engineer. The question is, what do you actually need to learn? I'm gonna break down the 4 core skills that separates people from making low $50K from someone making 6 figures, and I'm gonna be brutally honest about what good enough actually looks like because most people are learning way more than they actually need. Or some things are not good enough. Skill number one: advanced SQL. This is the gatekeeper. SQL is non-negotiable. If you can't write advanced SQL, you're not going to get hired for these roles, period. Advanced SQL means you know how to write window functions like RANK, DENSE_RANK, ROW_NUMBER, LAG, and LEAD. You know how to use common table expressions to organize complex queries, or you can use CTEs for hierarchical data. You can do complex joins and self-joins without breaking a sweat. This is the language of data modeling, and data modeling is a primary job of an analytics engineer. Companies like Apple expect explicitly require mastery of advanced SQL in their job postings. This is the gatekeeper skill. If you don't have it, you're not going to get through the door. Skill number 2, data storytelling and visualization, the decision driver. Here's where most people drop the ball. They think data visualization means throwing 10 charts on a dashboard and calling it done. Wrong. Absolutely dead wrong. Data storytelling is the ability to take complex information and turn it into a narrative that drives action. It is a skill that transforms raw numbers into million-dollar decisions. Let me give you a real example. A sales team was confused about why sales were dropping in one region. A good analyst built a dashboard showing the trend, but a great analyst told a story. They proved that the dip correlated directly to competitor delivery speeds. That was a narrative that led to a clear business decision: work with logistics to reduce delivery times and add real-time tracking. This isn't just reporting, that's driving strategy. Or take a marketing team seeing low conversion rates. A data story visualized a customer journey and proved users were abandoning carts at the payment page. The decision? Add trust to third-party payment options like PayPal and Google Pay to add flexible payment options. This is a skill that makes you valuable, not your ability to just write code, your ability to influence decisions. Skill number 3, strategic thinking. This is the value multiplier. This is what separates a tool operator from a problem solver. Strategic thinking is pure business acumen. It's understanding how the business operates and how to make profitable decisions. It's connecting every SQL query to the business bottom line and revenue impact. And here's the thing, this skill has a hard financial return. Professionals with strong business acumen command salary premiums of 15% or 25% higher than those who don't. Senior-level analysts earning $150K or more are valued specifically for their leadership and strategic thinking. This is a skill that allows you to translate data insights into actionable recommendations for decision makers. You're not just saying sales are down by 50%. You're saying sales are down by 15% in the West Coast region because we're losing traction with the high-value customers. Between ages 25 and 34, and here is a 3-step plan to get them back. And then here's a projected ROI. That is strategic thinking, and this is what gets you promoted. Skill number 4, automation with Python. So this is your efficiency superpower. Most analysts spend about 50% of their time on repetitive tasks— formatting, reconciling data, pulling the same metrics every week. I used to work with someone who used to automate all their reports with Python. They would save the organization many hours every week. And you know what happened? They became irreplaceable. You don't need to become a software engineer. You just need to learn practical Python for automation. Now here's what happens when you combine all of these 4 skills: advanced SQL, Python, strategic thinking, and data storytelling. That is the complete flywheel of the full-stack data analyst, and that is what makes you worth a high salary. All right, now let's bring it home. You have a choice to make right now. Path 1: choose the data scientist path, spend the next 2 years in grad school studying linear algebra and machine learning theory, a bunch of other stuff that no one cares about in the job market, and then go into $50K in debt, then face a ghost town of the entry-level job market that demands 5 years of experience that you don't have. Or path 2, build a full-stack analyst skillset, master SQL, data storytelling, strategic thinking, and Python for automation, and position yourself for a high-demand, high-impact, and high-paying role that's actively hiring right now. The choice is pretty clear to me. Not sure about you. I say stop chasing the dead-end title and start building the 4 key high-value skills that actually pays in today's job market. If you're ready to make the shift and you want the exact roadmap that my students use to land these roles without going back to school, click the link in the description and I'll show you the exact step-by-step process that we use to get people hired in 6 months or less. We can talk about how we can work together to fast-track your transition. Don't wait another month learning the wrong skills. The market is moving and you need to move with it. I'll see you in the next one.