In this career‑advice video, data‑analytics community leader Kedeisha Bryan explains why most aspiring analysts waste time on generic tutorial‑style portfolio pieces and outlines five high‑impact project types that actually get hiring managers’ attention. She first distinguishes learning projects (tool practice) from showcasing projects (story‑driven business problems). She then details each of the five portfolio project categories: (1) KPI card dashboards that answer a single executive decision question in seconds, emphasizing the need to lead with a business question, limit metrics to true KPIs, and provide contextual comparisons; (2) Root‑cause analysis projects that walk through problem definition, hypothesis testing, segmentation, and actionable recommendations, showcasing structured analytical thinking; (3) Domain‑specific projects that leverage the analyst’s prior industry knowledge to solve high‑value problems in sectors such as healthcare, retail, or finance, positioning the analyst as a day‑one contributor; (4) Consulting‑style recommendation projects that package analysis into a concise executive slide deck with clear actions, demonstrating the ability to translate data into decisions; and (5) Real‑world consulting projects where the analyst offers free 30‑day analytics consulting to small businesses or nonprofits to gain authentic experience, data, testimonials, and references. Throughout, Bryan interweaves personal credibility (her own transition from $8‑hour jobs to leading an 80,000‑member data community) and market data (e.g., 60% of hiring managers now prioritize business thinking). She ends with a call to action to rebuild the weakest portfolio piece that week and to follow her roadmap for landing a data‑analytics role.
Chapters
Most aspiring data analysts are building the wrong portfolio projects. You spend hours building dashboards, polishing your portfolio, and still get ignored because hiring managers keep seeing the same old tutorial follow-alongs, overused datasets, and copy and paste projects. So today I'm gonna break down the 5 types of data analyst projects that will actually make companies take you seriously, why they work, and how to build them so you can stop wasting time on projects that don't help and start building a portfolio that actually gets interviews. If you're a subscriber, welcome back. Now, if this is your first time here, I'm Kedeisha, and 6 years ago I was delivering pizzas and Amazon packages for $8 an hour. And today I am running a community of over 80,000 current and aspiring data professionals, and I have 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. And if you're ever interested in working with me to land your first role in data analytics, click the link in the description and let's get into it. Okay, so before we get into the 5 project types, there's one distinction that you need to understand. There are projects for learning and then there are projects for showcasing. A learning project is where you're practicing a tool, follow a tutorial, getting comfortable with SQL or Tableau. That is how you're building the skill necessary. But a showcasing project is completely different. That's where it's all about storytelling. It's about the business problem that you solved and the insights that you uncovered. Things that make someone stop scrolling through 100 applications and say, I need to talk to this person. Not every project that you build belongs in your portfolio. But these types of projects do. Okay, so project type number 1, the KPI card dashboard. Before we talk about dashboards, you need to understand how businesses actually run because this is the most important piece that people skip and why their dashboards usually look like decorations instead of functional tools. Every business, whether it's a hospital, retail chain, SaaS company, nonprofit, runs on KPIs, key performance indicators. Revenue growth, customer churn, cost per acquisition, readmission rates. A VP opens up a dashboard Monday morning to answer one question. Are we on track or off track? They need that answer in 10 seconds. When churn goes up 5%, that's not just a line trending in the wrong direction. That's also hundreds of thousands lost in revenue. When conversion rate drops from 4% to 3%, that can be the difference between hitting a quarterly target and explaining to leadership why you missed it. If your dashboard can't deliver that clarity very fast, it's just decoration. The market has shifted to reflect exactly this. 60% of hiring managers say business thinking has now become more important than ever in the last 5 years, and even 68% of chief data office say their top priority is improving how analytics are actually used inside their organizations. Companies don't want more chart makers. They want people who understand what the numbers mean and can organize them to drive decisions. Back in 2012, Jack Dorsey, the co-founder of Twitter at the time, shared that the lack of internal dashboards in the early days of the company caused a lot of confusion and bad decisions that were filled by opinions and not data. So when he first founded his company Square, he made sure KPI dashboards was a top priority, saying, The first thing I wrote for Square was an admin dashboard, and we have a very strong discipline within the company to log everything, measure everything, and test everything. We treat the dashboard analytics and the data as a product. We call it the inference team. Their job is to instrument all usage and infer all action. So here's the move most people miss when they build a dashboard. They lead with the data. The right move is to lead with a question because the question tells you which data matters and which is just noise. So before you open Tableau or Power BI, before you drag a single field onto the canvas, you need to be able to answer this. What decision does this dashboard exist to make easier? That's it. One decision. For a VP of sales, it might be, are we on pace to hit our quarterly target? And if not, where is it breaking down? Every visual on the dashboard either answers that question or it comes off. That means 3 to 4 KPIs, not 20. In marketing, you're probably looking at cost per lead, conversion rate, and customer acquisition costs. In operations, that can be on-time delivery, fulfillment time, and defect rate. Test every metric with this one question. If this number changes, does it change how someone spends their money or moves resources? If the answer is no, it's not a KPI. It's just noise, and you can remove it from the dashboard. Every number without context is useless. Conversion rate is 3.2% means absolutely nothing to anybody. So every KPI needs a comparison: current versus last period, current versus industry standard. Pick the one that makes the most sense for your business situation. So here's what I want you to sit with: the supply of technically capable people is going up every single month. So the question isn't whether you can build a dashboard; it's whether you can build one that makes a decision easier. That one shift. From displaying data to driving decision is what's separating the candidates who are getting filtered out at the resume screen and those who are getting calls. All right, so project type number 2, the root cause analysis project. Now this one is underrated and it might be the strongest thing you can put on a portfolio because it proves that you can think like an analyst and not just one who knows how to use tools. Every company has problems that they can't fully explain. Why did churn spike? Why are certain stores underperforming? Why did no-show rates jump 15% out of nowhere? And most analysts will tell you that churn went up and the VP already knows that, but what What they need is the why. What's driving it? Where is it concentrated? And getting to the why requires structured thinking, defining the problem, breaking it into components, testing hypotheses, and presenting a root cause with evidence. That sequence is a skill, and most people applying to data roles don't have it because they never built a project that required it. So you wanna start with a business problem that has real tension, not just analyze customer data, but why are customers leaving after the first 30 days? The more specific the problem, the stronger the project, and specificity signals that you understand how businesses actually think and make money and their challenges. Then you want to follow this flow: define the problem and the metric that you're tracking, segment the data by time period, by customer type, geography, product line, then identify the anomaly. Where exactly is the problem concentrated? Investigate the drivers. What changed? Was there a pricing update, a product issue, a competitive move? Then present your finding, and this is where most people skip, What the business should do about it. Your deliverable is going to be a 1 to 2 page write-up that walks through the full story: problem, analysis, insight, and next step. Someone should be able to read it in 3 minutes and walk away knowing exactly what you found and why it matters. The salary gap between tool users and problem solvers is real. Tool users will just get assigned work, but problem solvers will get asked their opinion. And once you become the person a manager calls on to when something is broken and nobody knows why, your value stops being tied to just your job title, and then it becomes to what you can figure out, and that is someone that no one can easily replace. And this project is the first place that you can prove that you can operate at that level. Now, everything I just described about root cause analysis will get you 10 times more powerful when you add one thing to it. If you're a career changer, this next type of project is one that I'd build first. 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 project type number 3, the domain-specific project. If you're a career changer, and I know a lot of you are watching this are, I need you to hear this clearly. Your biggest advantage is not gonna be in learning more tools. It's about the industry knowledge that you already have, and most people are sitting on it without realizing how much it's worth. Think about what a healthcare company actually needs. Not just someone who knows how to build a Tableau dashboard. Anyone can learn that. They need someone who understands patient flow, readmission metrics, staffing ratios, compliance language. An analyst with that context contribute week 1. An analyst who only knows the tools will need 3 months of ramp-up period to understand what that KPI even means. Your background isn't baggage, it is your unfair advantage. A domain-specific project is also how you can put it on display. So pick an industry that you're targeting or that you're coming from. Healthcare, build a healthcare project. Retail, build a retail project. Education, finance, HR, logistics, whatever your background is, build a project in that space. You already speak the language, the project is just an element that shows it. Then research actual problems that keep people up at night in that industry. Not surface-level stuff, but problems that are tied to the real money. Every industry has high-value problems that data is constantly used to solve. Find that one in your space and build a project around it. When your project is tethered to a problem at that scale, you're not just a student showing a project, you are an analyst showing what you can do on day one. And today, over 70% of job postings now prefer domain experts over generalists. A generalist can always learn another tool, but they cannot replicate years of industry fluency. That gap only guidance the longer that you stay in your domain. And this project isn't just a faster path to the job offer, it's part of the foundation of a career that gets harder to compete with over time. But domain knowledge alone won't just close the deal. You have to have the right industry. You can have the right industry context, the right data, and the right analysis and still not get the offer if you can't translate it for the person making the decision. Okay, so project type number 4, the consulting-style recommendation project. This is where most people will leave money on the table. Literally, a lot of aspiring analysts can do all the technical work, but very few can translate it into a recommendation that non-technical people can act on. And that gap can be a difference of $55K to $100K. In most companies, the analyst isn't the decision maker. Your job is just to make the decision maker's job easier. That means taking your analysis and translating it into plain language, organizing it into a clear narrative, and delivering it in a format that executives or managers can act on. If you can't do that, your analysis just dies in an Excel sheet. For this project, it ends in a mini executive slide deck, 3 to 5 slides, not a Jupyter Notebook, not an Excel sheet, and not a raw dashboard. A presentation a stakeholder can open, understand, and act on without you even in the room. Slide 1, action title. Lead with the answer first and support it with evidence. Every slide should drive toward a decision. If a slide doesn't move the audience closer to action, just cut it. That is discipline, knowing what to leave out. This is a skill that will separate junior analysts from people who get promoted into high-earning roles. Here's a lesson that I learned at my first data job: data does not speak for itself. An executive scanning a Jupyter Notebook or an Excel sheet at 6 PM before a board meeting is not going to act on anything that they see. A 3-slide readout with clear recommendations might change the agenda entirely. The analysts who move up faster on the corporate job ladder aren't always the most technically skilled. They're the ones who understand that the analysis is just the input, but the decision is the product. So when your portfolio includes a consulting-style slide deck with clear recommendations, you're not just showing technical ability, you are showing one thing most technical-capable people never develop, and that moves you from individual contributor to the person who influences what the business does next. Everything that we've talked about so far assumes that you have a dataset to work with, but this last project type removes that assumption entirely, and it's one that kills the I have no experience excuse for good. All right, so project type number 5, the real-world consulting project. Okay, so here's an honest truth. The reason that most people don't have experience experience isn't because no one will hire them, it's because they're waiting for someone to give it to them. Experience isn't something that has to happen for you anymore. It's something that you can go and create right now. There are thousands of local nonprofits and small businesses and even small to mid-sized content creators online sitting with data that they do not know how to use, and they will let you work with it if you just ask. So what I want you to do is pick 10 maybe small to mid-sized organizations, nonprofits, local businesses, whatever, and offer 30 days of free analytical consulting support. Out of those 10 outreach messages, at least one's going to say yes. They need help and no one else is offering it. Scope it tight. One question, 2 to 4 weeks. Your deliverables will be a dashboard and a consulting-style slide deck. When it's done, ask for a testimonial and ask for a reference. Real work, real data, and a real endorsement. No course can manufacture that for you, and no certificate replaces it. Okay, so after this video, don't just go watch another tutorial. Go look at your portfolio and pick one of the weakest projects that you have and rebuild it and replace it. One project this week. You already have what it takes. Now just go build the evidence. And if you want to see the exact roadmap to help you put all of this into practice, click and watch this next video.
