Brian Morrison

Here's a blog post about the one important skill I *didn't* learn in grad school... ...SQL! That's right, the same SQL that many data analysts take for granted was something I never started using regularly until I started work as a data scientist. However, looking back I see a lot of value that could have helped in my grad school and crime analyst days. Here I talk a bit about using SQL inside of R, and specifically how to use DuckDB to speed up the analysis of large tables. #duckDB #Rstats #datascience #SQL

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What are your data goals for 2025? I want to get deep into analytics engineering, here's my plan: 1) Learn the fundamentals of analytics engineering like Data Modelling, Incremental Tables, CDC, Version Control, CI/CD, and Semantic Layers. While I've explored tools like dbt and Airflow, it's important to focus on the fundamentals! 2) Expand my skillset by taking on more ambitious projects at work. This will enable me to apply the fundamentals I've learned and break (and then repair) things! 3) Keep up to date with the MDS; tools like dbt and Airflow are facing competition from newer ones such as SQLMesh and Dagster, it's important to keep up with industry trends. It's important to define your goals at the start of the year and have some KPIs which you can work towards, happy learning! #analytics #datascience #datajourney #dataengineer

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You know what's not taught well in data science programs and data science bootcamps? Messy data. In school, you get a dataset with carefully curated missing values you can find with methods you were taught. In the real world, that data lives in 17 different tables with various column names, different snapshot frequencies and no documentation. #datascience #analytics #data #newgrad

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Ever wondered what a CoxComb Chart is or how to create one? In my latest blog post, I explain how to read CoxComb Charts and how you can make your own version in Tableau. I've adapted a stunning template shared by CJ Mayes, who breaks down the complex math and logic behind this visualization in an easy-to-follow manner. Here is a direct link to his tutorial: https://lnkd.in/gbEFnpsn I have created two versions of the CoxComb Chart showing the Medals Won By Each Country in Olympic Games Paris 2024. The blog post includes detailed instructions on how to replicate these charts yourself. Read all about it here: https://lnkd.in/gvT3MNMj Comment which version do you like more? :) Version 1: https://lnkd.in/gze5Bjbv Version 2: https://lnkd.in/gi_ncGuC #CoxComb #Radials #Tableau #DataFam #Analytics #BusinessIntelligence #CreativeCharts #Data

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I have been utilizing and maximizing Tableau containers in building my dashboards at work but I have learned and appreciated it more during today's Next Level Tableau session with Andy Kriebel. Thanks, Andy and NLT-mates for the productive learning session! 🤗🙏🏼 #NLT #Tableau #containers

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❓ Did you know that Tableau's character sort is case-insensitive?   ⚖️ That means that it treats capital letters and lowercase letters as the same.   👍 That's probably a good thing and a useful default.   😀 The (somewhat funny) problem that I ran into is that has my RDBMS (Teradata) has case-sensitive sorting.   📃Capital letters sort first, lower case letters later.   ❌ I was testing my dashboard with some queries against the source data and many of my metrics were off for the first row.   🔍 In the Teradata tool, the text fields were also being truncated for display as in the "Output" section of the image.   👁️ So initially, I couldn't see the sort disparity.   👍Once I saw it, I wrapped my ORDER BY with an UPPER(variable), which eased the comparison, and everything was fine.   📝 Sometimes it's the little things (assumptions) that cause problems.   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #data #datascience #analytics #VizoftheRay

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🤔 Have you ever heard of a Ziggurat Chart or a Chichen Itza Chart?   📖 It's not really a recognized thing…   I made it as a gag to tease fellow tableau developer who had recently made waffle charts and plum pudding charts.   I figured, why not try a Triangle?   🤪 It doesn't really work that well, but it *does* have 100 squares, and it's a weird way to show a percentage.   🏈 This is a Throwback Thursday viz!   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

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The best data analysts aren’t just great with numbers, they’re great with people. Understanding the needs, challenges, and goals of your team and business leaders is just as important as the metrics and reports you create. This happened to me this week when I presented opportunities for metrics I thought might be important and the business leader met me with: "I need to do these other metrics first". Slow down. Listen before making suggestion or proposals. It's not that they don't need the metrics you are proposing, it's that some of the others are more important today. What’s one skill outside of data you think every analyst should have?" #DataAndPeople #LeadershipInAnalytics #GrowthMindset

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Turnover can either derail a project or be just a bump in the road. Losing critical knowledge is expensive—but it doesn’t have to be. With the right documentation practices, high turnover becomes manageable, not disastrous. Here’s my approach to handling turnover as a Data Scientist: 𝗣𝗠𝘀/𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗟𝗲𝗮𝗱𝘀: Capture the big picture—why the project matters, key deadlines, expected outcomes, and the essential stakeholders. Ensure every detail is documented so the project doesn’t stall when someone exits. 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀: Document pipelines, key tables, runtimes, and critical Airflow links. This allows anyone stepping in to pick up exactly where things left off. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀/𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀: Create a culture of documentation. Ensure data processes, methodologies, and insights are organized and accessible, so no knowledge is lost when team members move on. Being a Data Scientist isn’t just about crunching numbers—it's about being the safety net that keeps knowledge intact when your team changes. Technical skills matter, but adaptability and documentation make you indispensable. 👉 How do you handle turnover in your teams? #DataScience #Turnover #Documentation

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Very humbled and blown away to see Statistical Tableau featured as a book review from the Data Visualization Society. Huge thanks to Emmie Mercer, DBA, MSIS for the thorough review. How are you using #Statistics in your analysis? Let me know in the comments below and check out her review.

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I write tutorials every day to help you learn Tableau. Less than 2% of you use them. They're great for reference, but not as useful as they would be if you practiced. Take the content I give you and rebuild each chart or tutorial. Put in the reps. You'll improve dramatically. NOTE: Less than 2% is a guess, but I'm fairly sure it's accurate given the comments and DMs I get.

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🤔 Did you know that Tableau has no native way to move a reference line to the background?   😲 This is a bit surprising to me.   🔎 Consider bullet charts. They are very popular.   If you want to label the bar, no matter where you put the label, the value *could* be overlapped by the reference line.   That is not good.   I went with the simple solution (which is actually not pictured here): ✔ Adjust line opacity to 50% ✔ Darken the font of the bar label   There are other hacks out there involving dual axis. Feel free to research.   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

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If I had to start from 0 with Tableau here's what I'd do: (how I actually got noticed) → Put the majority of your learning time into your Tableau Public portfolio Here’s why: 1. **Instant Credibility**: Your Tableau Public portfolio acts as a live resume. Employers, clients, and peers can see your work, skills, and creativity in real time. No need to just talk about your expertise—you can **show** it.     2. **Community Recognition**: Tableau Public isn’t just a tool, it’s a community. Having high-quality visualizations can get you noticed, featured, and shared. This exposure can lead to new opportunities and connections that may never have come your way otherwise.     3. **Constant Learning & Improvement**: A public profile keeps you motivated to innovate. Each new visualization is an opportunity to experiment, learn new techniques, and raise the bar. The feedback and inspiration from the community only push you further.     4. **Visibility to Opportunities**: Recruiters, companies, and collaborators use Tableau Public to scout talent. A standout profile could open doors to projects, roles, or collaborations that others simply won’t get access to. The trick is to find a topic from interesting data sources that you enjoy.  You'll stick to it longer. 1. makeovermonday 2. data.world 3. kaggle This is exactly how I have landed interest in my Tableau expertise. And how I've been recruited for multiple positions. Pick a data source, viz it and sell your skills on Tableau Public.

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Wondering if you will ever break into data science? I've been there... SEVERAL times. Does this sound familiar? ▶️ Working through a YouTube tutorial ▶️ An issue comes up ▶️ You are now 17 tabs deep ▶️ Negative thoughts start to creep in The truth is... Imposter syndrome kicks in, even when the stakes are low. So how do you avoid this negative self-talk? ✨ Confidence ✨ Easy to say, right? Easy to build too. All you have to do is: ➡️ Keep doing what you're doing ➡️ Repeat several times That is literally all it takes. Instead of doing a tutorial once and forgetting about it. Do it 4 times and on your 5th attempt, add in your own twist. You KNOW if you did this, you would be an absolute beast at data science. (Almost as if you knew the answer the entire time 😉) So why aren't you doing it?

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The secret to leveling up your data career is working on your soft skills. It's not what the gurus tell you, but I don't think many of the gurus work in data. If you'd like weekly tips on how to communicate better with data or within organizations, you can subscribe to my newsletter. Every week has a story, some tips and an exercise to help you level up your communication skills. The next one comes out on Sunday and has five tips you can use to keep your audience's attention during your next presentation! Subscribe via the link on my profile, or via the link in the comments.

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Why I’m Switching to the Tableau Metadata API Quick tip: If you’re still using tableauserverclient to pull data, you’re missing out. The Tableau Metadata API is much more efficient. - It’s faster - It reduces server load compared to tableauserverclient - It gives you more detailed insights with less hassle If you’re managing a lot of workbooks, make the switch—it’ll save you time and help keep your dashboards clean. Trust me, the difference is huge!

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Data is not as complicated as you think. Data science, data engineering, analysis, visualizations... None of them are as complicated as you think. I don't mean they're easy to do. I also don't mean the people who do them aren't smart, or you don't need specialized skills to do it. It's hard to do, and it does require some specialized skills, but so does any space. Still, data isn't as complicated as you think. So why does data SEEM complicated? Because we suck at talking about it. I don't mean we never talk about it. We talk about it a lot. We suck at bridging the gap between data projects and business. We suck at explaining results in a way that makes them meaningful, and we suck at operationalizing projects with promising results. Okay, we don't suck, but we could do a better job. Lets connect and talk about how we can get better. #datascience #communication #dataliteracy

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