In this 15‑minute personal‑narrative video, Kedeisha Bryan recounts two years of “motion” – endless tutorials, certifications, and tool‑learning that never led to a job – and contrasts it with a few months of “action” that finally secured her a data analyst role. She explains why most aspiring analysts stay stuck, referencing James Clear’s Atomic Habits to illustrate the difference between motion (feeling busy without progress) and action (directly solving real business problems). Bryan then outlines four concrete steps that transformed her job‑search: (1) Prioritize employer‑demanded tools (SQL, Excel, Tableau/Power BI) based on a study of ~4,000 job postings; (2) Choose a single industry, research its high‑impact problems, and focus learning on those contexts; (3) Gain real‑world experience by offering free data‑analysis help to a local nonprofit or small business, turning messy, unscripted data into a portfolio project; (4) Present findings as decision‑focused stories – start with a clear business impact statement, then support it with clean visuals, rather than walking through raw spreadsheets. Throughout, she shares anecdotes (e.g., a former student Jacinda’s turnaround, a food‑bank project) and urges viewers to follow a pinned link for detailed step‑by‑step resources. The core message is that targeted, action‑oriented learning beats endless consumption of tutorials.
Chapters
I wasted 2 years learning data analytics, learning every tool, watching tutorials. I actually thought I was making real progress, but when I started applying for jobs, over 50 applications later, not a single reply. So I stopped, didn't touch a single tutorial for over 2 months, and I tried something completely different. What I learned in those few months taught me way more about data analytics than the 2 years before it. So in this video, I'm gonna show you exactly what I did so you can become a data analyst in months and not years. But first, I want you to understand what kept me on the wrong path. Clear, the author of Atomic Habits, captured this problem perfectly. When I first read it, I couldn't stop thinking about it, and it explains exactly why so many people waste years trying to break into data analytics. There are 2 types of progress people make. First is motion, and it looks like this: you're moving, you're learning, feels like something's actually happening, but you look up and you're in the exact same spot you were when you started. This is a cycle where most people stay stuck, and this was me when I first started. I'd finish a course, watch a lengthy tutorial, and then feel really good about myself. Then I'd start another course, 4 courses, 3 certifications, Certificates, and I generally thought I was making progress. But when I would finally start applying for jobs, nothing. I was stuck in this constant cycle and not getting any further. The second type of progress is action. Action is like walking in a straight line. Every step that you take actually takes you closer to your goal. In data analytics, that means solving actual business problems, not pretend ones from tutorials. And this is what changed everything for me. This is when I stopped watching tutorials and started doing the things hiring managers actually was looking for, proving that I can solve actual real problems. I spent the next few months working on this type of project, and it taught me way more than 2 years of tutorials ever did. Now, am I saying you shouldn't be learning tools or watching tutorials at all? Of course not. You do need them and practice using them, but the mistake is staying in motion for years and thinking that the next course or tutorial is the one that's gonna finally get you hired. It's not. Action is. So how do you exactly take action the right way without wasting more time going in the wrong direction? Here are 4 steps I followed from day one. All right, so step 1 is simple, but get this wrong and you'll stay stuck in motion for years. I did a study a few months ago where I looked at almost 4,000 job postings, and here's what I found. SQL showed up over 78% of the time, Excel 70%, and Tableau, Power BI about 60%. R barely registered, machine learning almost nonexistent. I remember I was spending months on R, machine learning, and even learning Python for data science, stuff that barely showed up in any job postings. Meanwhile, I was mediocre at SQL and Excel, the stuff that almost every job required. I was spending months getting good at the things that didn't matter and staying bad at the things that did. So just focus on SQL, Excel, Tableau, or Power BI and get good at those 3 and move on. You don't want to get trapped in motion. And this is exactly what Jacinda did in my program. Spent 8 months learning R and couldn't get a single interview. She barely knew Excel and Tableau. So I told her to stop and just get good at SQL. And 90 days later, she landed her first role in data. Okay, step 2. And this is why most people stay overwhelmed. I was trying to learn everything at once. Every tutorial I watched was a different industry, a different use case. I had bits of knowledge about everything and deep knowledge about nothing. So I ended up sounding like a generalist who couldn't actually help anyone specific. So what I did was I stopped and I picked one supply chain. I'd worked in warehousing and logistics, so I skipped the few months of learning an industry from scratch. But here's what actually changed everything. Instead of opening up another tutorial, I started researching what supply chain companies actually cared about. Case studies, articles, job postings. And newsflash, it's not pretty dashboards and colorful charts. They care about one thing: why are deliveries late and how do we fix it? An actual business problem they actually need to solve. And so here's the thing, I look back at every project I ever built. Every single one solved a problem that I made up, not a problem anyone in supply chain was actually asking about. Employers don't hire a person with the best SQL skills or Excel skills or whatever. They hire the person who understands their problem the most. So step 2 is pick one industry and learn what those people actually care about. Learn about the high-value and high-leverage problems that keep the executives up late at night. All right, quick pause. Now, if you want to land your first job in data analytics and avoid the lengthy mistakes that I did, click the link in the pinned comment where you can find the exact steps that my students use to land their job in data analytics without going back to school. All right, now let's get back to it. All right, so now step 3 is one that made everything else actually matter. I did everything right— learned SQL, picked an industry— but I was still getting ghosted. My resume looked like every other career changer's. Courses, certificates, practice projects, but nothing real. So I tried something completely different. I found a local food bank, sent them an email saying, hey, I'd love to donate a couple hours a week just to help you get sense of your data at no extra cost. They replied the same day, and that's when everything got real. Their data was a complete mess. Missing columns, dates from different formats, duplicate entries everywhere. But here is why that's a good thing. There was no tutorial for this. I had to figure out what actually mattered, what was garbage, and what I could turn into something useful. One real project taught me more Than every other course I'd ever taken. And suddenly I had something unique to show on my resume. Not just SQL, but I helped a food bank figure out how to serve more people with the same resources. That is what made me stand out. So if you're wondering where to find one, Google local nonprofits or small businesses in your area. Food banks, churches, local gyms, anyone with data and no one to look at it. Just email as many as you can. One will say yes. Okay, so this brings us to step 4. I finally had a real project under my belt, but there was still something that kept messing up my interviews and even messing up some of my early meetings on the job. Whenever I presented, I would just open up a spreadsheet and just start walking people through it. I generally believed that if I showed enough of the raw data, the rows, the columns, the formulas, the filters, people would automatically see the value. In my mind, the data spoke for itself, but the problem was it didn't. I would scroll through the tabs, explain how I cleaned the dataset, maybe even sort the revenue or highlight a few changes over time. Now technically, everything that I was saying was correct. The analysis wasn't wrong, but there was no clear takeaway, no clear direction And no framing of why any of it actually mattered. People would nod, they'd say okay, and then we'd move on. Nothing changed. Then I watched a senior analyst present something very similar, but she was actually working with the same dataset that I was using. But she didn't start with a spreadsheet. She started with a slide with a clean, simple chart with one bold sentence at the top: customer churn increased 18% due to onboarding delays, and here's how we fix it. Before she showed any numbers, she framed the problem, she quantified the impact, and she signaled that a solution was coming. Then she walked through the visual, not every data Not every transformation that she did behind the scenes, just the part that supported the decision that needed to be made. The energy from the room shifted. People leaned forward. Leaders started asking questions about implementation and clarifications. That's when I realized the difference. I was presenting my work like a technician trying to prove that I had done the work, while the senior analyst at the time, she was presenting like a consultant focused on what decisions needed to happen next. I thought showing the spreadsheet would demonstrate the effort. She showed insight and that demonstrated value. So that changed my approach completely. Instead of opening meetings with the raw data, I started opening with a conclusion. Instead of saying, here's a Q3 sales file, I will say something like, we're losing $42,000 per month from repeat customers due to delayed follow-ups, and here's 2 changes that could fix it. Then I would use clean visuals to support that claim. It was the exact same analysis and the exact same dataset, but the reaction was completely different and it didn't require another course, another certificate, or 6 more months of learning. It just required shifting from just showing data to showing a story. I spent 2 years stuck in motion and a few months in action, and only one of them got me hired. But what do you actually hand them when you're done? Most people wing it. I made a video showing you exactly what to build and how to structure it so it actually gets you hired. So watch this video next.
