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Business/Data Analyst with 5+ years of experience interpreting and analyzing data to…
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I absolutely love stories like this by Lee. It doesn’t matter where you are in your career – it’s never too late to switch or start. You don’t…
I absolutely love stories like this by Lee. It doesn’t matter where you are in your career – it’s never too late to switch or start. You don’t…
Liked by Tetchi Assamoi
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L’ émotion qui m’a étreinte à tel point que je n’arrivais même plus à faire mon speech, m’a aussi empêchée de dire merci Le Lycée de jeunes filles…
L’ émotion qui m’a étreinte à tel point que je n’arrivais même plus à faire mon speech, m’a aussi empêchée de dire merci Le Lycée de jeunes filles…
Liked by Tetchi Assamoi
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We are hiring! If you, or someone you know is a Digital Analytics and Marketing Performance professional, consider Brinks Home Security via the link…
We are hiring! If you, or someone you know is a Digital Analytics and Marketing Performance professional, consider Brinks Home Security via the link…
Liked by Tetchi Assamoi
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Sawankumar Jondhale
I spoke to a candidate in the UK last week. He said something that stayed with me: “I’ve done everything… but I’m still not getting interviews.” He had: • learned SQL • built dashboards in Power BI • completed multiple courses On paper, everything looked fine. But when I asked him one simple question: “Walk me through one project you’ve done.” He paused. Then said: “I built a sales dashboard.” That was it. No explanation. No insight. No story. And that’s where the real problem was. Because in today’s UK job market… It’s not enough to just build something. You need to explain what it means. For example: Instead of saying: “I built a dashboard” Say: “I analysed sales data and found that a small group of products was driving most of the revenue, which could help the business focus its strategy.” Same project. Different impact. That’s what recruiters remember. Not tools. Not dashboards. But clear thinking. If you’re trying to break into Data Analytics: Don’t just practice building. Practice explaining your analysis. That’s the skill most people ignore. And it’s often the reason they don’t get shortlisted. Be honest 👇 What do you find most difficult right now? A) Explaining projects clearly B) Building strong projects C) Getting interview calls D) Understanding business problems Comment A / B / C / D If you want help improving this: I work with learners in a 1-to-1 mentorship program where we focus on: • real-world projects • explaining insights clearly • resume improvement • interview preparation Comment MENTOR or DM me “MENTOR”. #DataAnalytics #UKJobs #PowerBI #SQL #DataCareers
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Mudit Agarwal
Bagging vs Random Forest — A quick note for data science practitioners Bagging is a powerful ensemble technique that allows the use of any base algorithm with n number of estimators. The idea is simple: build multiple models on different bootstrap samples and aggregate their predictions to improve stability and reduce variance. Random Forest, on the other hand, is a specialized implementation of bagging where the base estimator is specifically a Decision Tree, and multiple trees (n estimators) are trained to form the ensemble. A few key concepts behind these methods: • Bootstrap Sampling If N is the total number of observations and n is the number sampled for training each estimator, then n ≤ N, since each bootstrap sample is drawn from the original dataset. • Out-Of-Bag (OOB) Error OOB error estimates model performance without a separate validation set. It is calculated as the mean prediction error for each training sample using only the trees that did not include that sample in their bootstrap dataset. • Handling Imbalanced Data In Random Forest for binary classification, the "class_weight" parameter becomes very useful when classes are imbalanced. By assigning higher weight to the minority class, the model pays greater attention to it during training, helping improve predictive performance. Ensemble methods like these beautifully demonstrate how combining multiple imperfect models can produce a stronger and more robust predictor. #MachineLearning #DataScience #RandomForest #Bagging #AI #Statistics
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Nishank Kumar
🚀 Your Data Analyst Career Roadmap 🚀 From mastering SQL and Python to building dashboards and telling data-driven stories, this roadmap highlights the journey to becoming a successful data analyst. 💡 Key Takeaways: ▪️ Blend technical skills with business understanding ▪️ Master data processing & visualization ▪️ Develop strong communication, problem-solving, and critical thinking ▪️ Keep learning and growing - certifications and experience open new opportunities Whether you’re starting out or leveling up, every step in your analytics journey brings you closer to driving impactful insights and smarter business decisions. 📈 💬 I’d love to hear your thoughts! Drop your comments or insights below and let’s learn from each other. #DataAnalytics #CareerGrowth #SQL #Python #PowerBI #Tableau #DataVisualization #BusinessIntelligence #CareerRoadmap
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