Daniel Timmermann

This is so great, initiative and effort are so powerful.

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MS. Excel Shortcuts

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🚩 We Don’t Gatekeep Here. Free Data Science, Analytics, and Engineering Books 📍Over the years, I’ve built quite a collection of books. Some I bought, some were passed down to me by my mentor, and others were thoughtful gifts. These books have been my go-to references countless times, and now, as part of this amazing community, I want to share some of them with you. 📍But don’t just collect them, make sure you actually read them or refer back to them when you need to. 📍Instead of dropping your email in the comments, please fill out the form, the link is in the comment section and I’ll send a few your way. 🚩One thing to note: I’ve got a mix of Data Science, Analytics, and Engineering books, so I’ll send them as they are, no time to sort them out right now. ♻️ Report to a fellow bookworm #dataliving #favouribude

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📊 **Poll Time!** 📊 **How does your team use Tableau Server?** 🗳️ Take a second to share your experience: - 🔹 **Data Collaboration** – Centralizing and sharing dashboards - 🔸 **Data Governance** – Controlling access and ensuring data security - 🔹 **Automated Reporting** – Scheduled updates and alerts - 🔸 **Self-Service Analytics** – Empowering users to explore data independently **Why Tableau Server?** For organizations focused on **data-driven decisions**, Tableau Server is a game-changer. It allows teams to **collaborate**, access **real-time insights**, and ensure data **security** across departments. By centralizing dashboards and setting up scheduled updates, teams can work with confidence, knowing they’re using the latest data. The result? Faster, smarter decisions and a more connected, data-driven culture! 💬 Drop your votes and share any unique ways your team uses Tableau Server. Let’s learn from each other! #TableauServer #DataCollaboration #BusinessIntelligence #DataGovernance #RealTimeData #SelfServiceAnalytics #SeniorBIDeveloper

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🚀 Exploring Lasso Regression Residuals with Yellowbrick! 📊 I recently used Yellowbrick to create a residuals plot for a Lasso regression model with California housing data. This visualization makes it easy to see how well the model predicts housing prices by comparing the actual vs. predicted values! Here’s how I did it: from sklearn.datasets import fetch_california_housing from sklearn.linear_model import Lasso from sklearn.model_selection import train_test_split from yellowbrick.regressor import ResidualsPlot # Load the California housing dataset data = fetch_california_housing() X, y = data.data, data.target # Split the data into training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Create and display residuals plot visualizer = ResidualsPlot(Lasso()) visualizer.fit(X_train, y_train) visualizer.score(X_test, y_test) visualizer.show() Why Use Residuals Plot? 🤔 Residual plots help us understand if the model predictions are accurate or if there’s room for improvement. Ideally, the errors should look random, without patterns. What is Lasso Regression? 💡 Lasso regression is a type of regression that simplifies models by reducing some feature weights to zero, making it easier to interpret. Tools Used: Yellowbrick for visualizations Scikit-learn for data and model handling Data visualization like this can make it easier to improve and understand models. Give it a try and see the difference it makes! Inspired by Joachim Schork post, #MachineLearning #DataScience #Python #Yellowbrick #LassoRegression

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I make more than 5X the comp of my first data job. And I produce much more than 5X the "value" I used to. But I can't honestly say my work is 5X more fun, gives me 5X more flow state, or produces 5X the inner satisfaction. With the money comes 5X the headaches, 5X the administration, and 5X the ambiguity. This is the dirty secret of leadership. It's something no one talks about. (Until they quit.) And this, dear reader, is a problem. Especially for my friends in data science management. We're not exactly the most...you know...emotionally in-tune kind of people. But this feeling, if the conversations I have are to be trusted, is almost universal. And it has created an epidemic of isolation, loneliness and overwhelm among data science leaders. Especially new ones. --- To be clear, I like my job a lot. What keeps most of us going are what I call "the Two I's". The work is: - Interesting - Impactful Most C-suite leaders talk about their work in the same way: "interesting" and "impactful." (Even while most secretly wish they could take 6 months off.) Here's the truth: --- This is not enough. An "interesting" job is NOT a fulfilling job. An "impactful" job is NOT a fulfilling job. The work must almost be en-joy-able: ABLE to experience JOY IN yourself. The more joy you feel: - the more sustainable the job - the longer you'll last, and - the more effective you'll be --- If you enjoy being a hands-on data and AI practitioner, know that it is possible. But, leadership requires more consistent job shaping. I create the feeling of flow in my role by treating leadership like an engineer: - I view challenges like a system. - I solve problems from first principles. - I consolidate my non-meeting time into blocks like a "maker." - I generalize what I learn about strategy, communication, and leadership into reusable frameworks. And, I share my learnings online with you. --- As you grow in your career, don't just level up your income. Level up your enjoyment, too! To #thinksenior is not easy, but it is worthwhile. How do you find joy in your leadership role? Comment below. Good luck!

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📌 Don’t Make This Career Mistake as a Data Scientist, Analyst or Engineer Someone recently asked me, “What’s one lesson you’ve learned in your career?” That question made me reflect, and I realized there’s one mistake I’ve seen that can really hold you back. 📍 Getting Too Comfortable Remember when you were hustling hard to land that dream job? You were learning, reading, taking courses, and leveling up every chance you got. Then, you finally got the job. And what happened next? For many people, that hunger stops. You feel like you’ve arrived, so you stop putting in the effort to grow. You get too comfortable. 📍But if you stop learning, you stop growing. And if you stop growing, you start falling behind. 📌In today’s fast-paced world, the skills that got you the job might not be enough to keep you relevant in a few years. Technology evolves, industries shift, and staying stagnant can make you redundant faster than you think. 📍How to avoid getting too comfortable: 1. Keep Upgrading Your Skills: Think of learning as part of your job. Identify new tools or skills in your field and commit to mastering them. Even just two hour a week can make a huge difference. 2. Set Career Goals Beyond Your Current Role: Ask yourself: Where do I see myself in 3-5 years? What skills or experiences do I need to get there? Use your current role as a stepping stone, not a destination. 3. Stay Curious and Open to Change: Pay attention to trends in your industry. Is there a new technology gaining traction? A process everyone’s adopting? Start exploring it now so you’re ahead of the curve. 4. Surround Yourself with Growth-Minded People: Your environment matters. Connect with peers and mentors who push you to keep growing. 📌 What’s one career mistake you’ve learned from or think people should avoid? ♻️Repost so others can learn #dataliving #favouribude

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You're hired as a Data Scientist to solve specific business problems. So think about what types of problems you want to solve, and then build projects related to those problems (aka Tailored Projects). Otherwise you'll end up applying for any Data related role just so you can 'get something' and before you know it, you've applied for hundreds of jobs with no response. Tailor your projects to the jobs you actually want. To help you do this, I'm sharing my ultimate guide to Tailored Projects on Wednesday the 10th July, 09:30 am What would you like to see in the guide?

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Tips for learning data science on your own: ▶ Have a roadmap, but chunk it into small pieces so that you don't feel overwhelmed and stop moving forward ▶ Test different learning methods to see what medium you are most likely to stick with for the long-term because you've got a long ride ahead of you (e.g., video courses, text-based coding tutorials, books) ▶ Do a little bit almost every day (e.g., practice coding, read a book chapter), but not too much - burnout comes swiftly and mercilessly ▶ If you want to advance into a data science role at your company, find out what technologies are being used first - nothing like learning R only to find out that your company is transitioning to Python ▶ Don't spend too much time learning theory, it's better to dive into less-than-perfect practice as soon as possible, even if you don't feel ready ▶ Explicitly ask people to be your mentor and give them a good reason to do it (even if it's just the satisfaction of helping someone grow) #DataScience #UpSkilling

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I started learning Python using Mosh Hamedani "Python for Beginners", and it's surprisingly very rewarding when you figure out a problem all on your own! 😁 His solution was way more simplified than my own, but still great practice! Here's my little weight conversion program 🤗 #python #programming

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💡 **Unlocking Hidden Gems in Tableau Desktop** 💡 Did you know that **Tableau Desktop** has some powerful features that often go unnoticed? These “hidden gems” can make a big difference in how we build and interact with dashboards. Here are a few that I’ve found incredibly helpful: 1️⃣ **Duplicate as Crosstab**: Ever need to view your visualization in table form? Right-click and select "Duplicate as Crosstab." It’s a fast way to create a detailed view, especially for data validation! 2️⃣ **Custom Data Roles**: You can map fields to specific geographic roles (like Zip Codes or States) to boost location-based insights. Perfect for adding context to regional data with just a few clicks! 3️⃣ **Performance Recorder**: This tool allows you to analyze how long each element of your dashboard takes to load. Use it to identify and optimize slow-loading charts, improving the end-user experience. 4️⃣ **Show/Hide Containers**: Create cleaner dashboards by adding a "show/hide" button for containers. This lets viewers expand sections only when needed, maximizing space and keeping dashboards uncluttered. 5️⃣ **Table Calculations for Dynamic Insights**: Table calculations allow you to create dynamic, context-based metrics like running totals or percent changes without altering the underlying data. There’s always something new to learn in Tableau Desktop that can help us create even more **effective and efficient** visualizations! 💬 What’s a hidden feature in Tableau Desktop that you can’t live without? Let’s share tips and boost our Tableau skills together! #TableauDesktop #DataVisualization #TableauTips #BusinessIntelligence #HiddenFeatures #DataEfficiency #SeniorBIDeveloper #TableauDevelopment

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Data Science Bootcamps are bad. Here's why they get things ass backwards: They take this approach: 1) Learn skills via courses 2) Demonstrate skills via projects 3) Apply for jobs People have got jobs using this approach, but with the job market more competitive than ever a new approach is needed. I've talked to graduates from bootcamps (who use this approach) and are struggling to get jobs. The reason they struggle is they only think about what jobs to apply for AFTER they've done their projects. This means they have a portfolio of random projects unrelated to the job (think titanic, Boston housing, movie sentiment analysis...). With hundreds of aspiring Data Scientists applying for the same jobs, it's no wander they don't stand out. Here's what you should be doing instead: 1) Research and think about the industry you want to work in (e.g., healthcare, project management, nutrition) 2) Build Tailored Projects 3) Apply to jobs in that industry Employers will immediately see you've taken care with your application and care about the problems faced by the business, instead of being another random Linkedin easy apply. That's what my mate Lekan is currently doing - he wants to utilise his experience in project management o get a Data Science job in the same field, so we're building a Tailored Project to predict which major construction projects (e.g., HS2) are likely to fail and need support. So don't listen to bootcamps - focus on the industry you want to work in and backsolve from there. And that's before I get started on all the unnecessary stuff they teach you... If you want to learn more about how to do this, you can learn more via my newsletter: https://lnkd.in/eTAmRsDu (P.S you'll also get a free guide on Tailored Projects/the lowdown on my love life ;) )

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Also, it's about applying them effectively in real-world scenarios. Don’t just focus on adding more tools to our resumes. Instead, spend time effectively by solving real data challenges. Practice building data pipelines, optimizing queries, and cleaning datasets. As technology evolves, so will the tools, but our ability to grasp core principles and adapt them will make all the difference in careers.

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Aspiring Data Scientists shouldn't apply to 1000s of jobs... They should only apply for 10. Here's why: If you're lucky enough to get interviews for any of them, your potential employers can tell if you genuinely want to work there or if you applied just to 'get your foot in the door'. Interviews aren't just to show off your skills. They're not just to regurgitate your STAR answers. They're to show you want to work there. That you're willing to go above and beyond the requirements. You can only be keen in interviews if you only apply for jobs where you could ACTUALLY imagine yourself working there. Basically, if the job isn't a 'FUCK YES' it's a no (thanks Mark Manson for this analogy). So don't LinkedIn easy apply to every job out there. Do this instead: 1) Choose an industry you want to work in (e.g., healthcare) 2) Build Tailored Projects for those sectors 3) ONLY APPLY FOR JOBS IN THAT INDUSTRY By targeting jobs and sectors which are a 'fuck yes' you'll actually come across as keen in interviews. And with the right Tailored Projects, this plan will make you a good fit for each role you apply for. This means you only need to apply for 10s of jobs, not 1000s. So pick industries you're passionate about, build the right Tailored Projects and then apply to relevant roles. P.S you can learn more about Tailored Projects here: https://lnkd.in/eTAmRsDu

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🚩 New Day, New Data Tool.. How Can We Keep Up Without Losing Our Mind 📍 I saw a comment recently where someone was venting about the number of tools we’re expected to learn. They said something like, “Today it’s this tech stack, tomorrow it’s something different!” And honestly? While it was funny, I completely understand the frustration. 📌How you can manage this without feeling overwhelmed? 1. Focus on the Core Skills: Start with the foundational tools and concepts that will always be relevant: → SQL for data querying and manipulation. → Python or R for analysis and modeling. → Power BI or Tableau for visualization. → etc 📍These are the evergreen skills that almost every data job will require. Build a solid foundation here before chasing the latest trends. 2. Don’t Jump on Every New Trend: It’s tempting to learn every new tool, but not everything will be relevant to your work or career goals. Ask yourself: → Does this tool align with the type of projects I want to work on? → Is it widely adopted in the industry I’m targeting? 📍If the answer is no, it’s okay to skip it. Focus on what will add the most value to your specific path. 3. Learn by Prioritizing Projects: Instead of learning tools randomly, let your projects guide you. →  If you’re working on a data pipeline, you might pick up Apache Airflow or AWS Glue. →  If you’re learning machine learning, explore TensorFlow or PyTorch. 📍 Learning tools in the context of solving real problems makes the process easier and more meaningful. 📌 The field of data is constantly evolving, and there will always be new things to learn. It’s important to stay curious, but don’t let the fear of missing out (FOMO) distract you from mastering the essentials. 📌 It's great to stay updated on trending tools so you can engage in meaningful conversations with other data professionals. However, that doesn’t mean you need to learn everything all at once. Take your time to focus on what aligns with your goals and build your knowledge step by step. ♻️ Repost so others can learn. #dataliving #favouribude

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As data professionals, it’s crucial to prioritize eye care. Our work often demands long hours in front of screens, which can significantly impact our eyesight in both the short and long term. I had a personal experience recently that reinforced this. After spending over four hours writing queries without using my prescribed glasses, my vision suddenly failed me just minutes before submitting a project. I couldn’t see anything on the screen, and this was quickly followed by a severe migraine that lasted for three hours. I’m sharing this to remind us all to take our eye health seriously. As data professionals, your eyes are your most essential tools—before Python, SQL, Excel, or Power BI. Take breaks, use protective eyewear, and practice the 20-20-20 rule to safeguard your vision. Let’s work smarter and healthier! #datascience #datascientists #dataanalytics

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Your Data Science Career depends on one thing. And it's not doing more courses: It's your willingness to have uncomfortable conversations. Especially around taking on feedback. A few months ago, one of my colleagues said I could be finishing work more quickly. And whilst I didn't want to admit it he had a point. I like a daydream and doing things at my own pace (I've just started a book about eliminating hurry). And the stakeholder value won't deliver itself... So I made an effort to finish work more quickly. My colleague recommended I set deadlines for myself. A few weeks later, another colleague commended my quick turn around times! So don't be scared to have tough conversations. You'll thank yourself later. If you liked this, I share more advice from my 3 years in Data Science in my newsletter. You can check it out here: https://lnkd.in/eTAmRsDu

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