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#Python List Vs Dictionary Reel by @jessramosdata (verified account) - Comment "data" for my intro to SQL course, perfect for complete newbies to build your first project in 30 min. Most common data interview question ⬇
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@jessramosdata
Comment “data” for my intro to SQL course, perfect for complete newbies to build your first project in 30 min. Most common data interview question ⬇️ save this for later! One of the most common interview questions is this: What’s the difference between ROW_NUMBER, RANK, and DENSE_RANK? Most business problems only need ROW_NUMBER, but for some reason hiring managers are OBSESSED asking this one!! • ROW_NUMBER: sequential ranking, no duplicates, no skipped numbers • RANK: duplicates share the same rank, but numbers get skipped after ties • DENSE_RANK: duplicates share the same rank, no skipped numbers 🏷️ data analytics, data analytics project, data project
#Python List Vs Dictionary Reel by @koshurai.official - 🚀 Stop chasing "better" models. Start building better features. Most Data Science students spend 90% of their time tuning hyperparameters. But here'
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@koshurai.official
🚀 Stop chasing "better" models. Start building better features. Most Data Science students spend 90% of their time tuning hyperparameters. But here’s the truth: A simple model with great features will beat a complex model with bad features every single time. I put together this animation to break down the 7-Step Feature Engineering Workflow that actually moves the needle: 1️⃣ Raw Data: It’s always messy. Embrace the chaos. 2️⃣ Data Cleaning: Handle those NaNs before they break your logic. 3️⃣ Categorical Encoding: Turning text into numbers so the math can happen (One-Hot Encoding is your friend!). 4️⃣ Feature Scaling: Don’t let "Income" dominate "Age" just because the numbers are bigger. 5️⃣ Feature Creation: This is where the magic is. Combine variables (like Height + Weight = BMI) to give your model new insights. 6️⃣ Feature Selection: More isn’t always better. Drop the noise. 7️⃣ Model Ready: Clean, scaled, and optimized. Feature engineering isn't just a step in the pipeline—it's where the domain expertise meets the data. Students: Which of these steps do you find the most challenging? Let’s discuss in the comments! 👇 #DataScience #MachineLearning #FeatureEngineering #Python #AI
#Python List Vs Dictionary Reel by @dswithdennis (verified account) - Sentence embeddings encoding full sentences for comparison tasks
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@dswithdennis
Sentence embeddings encoding full sentences for comparison tasks
#Python List Vs Dictionary Reel by @dswithdennis (verified account) - Automated systems streamline repetitive tasks with precision
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@dswithdennis
Automated systems streamline repetitive tasks with precision
#Python List Vs Dictionary Reel by @itsdatagirl - Know your customer. Who is this data for? Before I build any dataset, I ask one thing: Who's going to use it? Because the "customer" determines ever
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@itsdatagirl
Know your customer. Who is this data for? Before I build any dataset, I ask one thing: Who’s going to use it? Because the “customer” determines everything: structure, complexity, even data types. Data for Analysts & Data Scientists → easy to query → no unnecessary complexity → minimal exotic types Data for Data Engineers → compact → nested structures are fine → optimization matters Data for ML models → depends on the model → feature structure > human readability Data for Clients / non-technical stakeholders → straightforward → clean & interpretable → easy to trust Same raw data. Very different design choices. Data modeling isn’t just technical work. It’s context work. Would you agree? #dataengineering #datamodeling #analyticsengineering #machinelearning #100daysofdataship
#Python List Vs Dictionary Reel by @maggieindata (verified account) - This is a commonly asked question in data science and data analytics interviews! How would you evaluate if a product truly drove improvement? Follow
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@maggieindata
This is a commonly asked question in data science and data analytics interviews! How would you evaluate if a product truly drove improvement? Follow @maggieindata for more data science, tech, and AI content #product #datascientist #careerintech
#Python List Vs Dictionary Reel by @crack_with_aj - You don't need Al! You need to start small! There's no reason to jump straight to Al, LLMS, agents or RAG, 90% of the data in the market is tabular a
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@crack_with_aj
You don’t need Al! You need to start small! There’s no reason to jump straight to Al, LLMS, agents or RAG, 90% of the data in the market is tabular and requires simple but robust solutions. On top of that some people jump in without a plan or understanding what these skills are or what they do in the market. Learn Python basics and write clean code Practice in Google Colab or Jupyter Notebook Get comfortable with NumPy and Pandas Visualize with Seaborn Take Stanford’s free Machine Learning course Master core algorithms like Logistic Regression, SVMs, and Decision Trees. Practice with projects like Titanic or Wine Quality to learn the full workflow You won’t be building your portfolio in 3 months. Instead take that time to get comfortable with these tools, learn key skills and train on the most in-demand skills. Follow @crack_with_aj to master DIY data science on your own, become a builder, Al-powered and go from a learner to earner. I created a data science guide just for you, comment DATA and I’ll send it your way!
#Python List Vs Dictionary Reel by @phoebeslifeindata - you're not bad at SQL and you're definitely not behind ☁️ most tutorials teach you what SQL queries do, not when you'd actually use them. That's why
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@phoebeslifeindata
you’re not bad at SQL and you’re definitely not behind ☁️ most tutorials teach you what SQL queries do, not when you’d actually use them. That’s why it feels like you’re learning a lot but nothing is clicking (believe me I’ve been there!) in real data jobs, you won’t always be asked to just write specific queries. you’re asked to answer business questions and SQL is the tool you’ll use to get there. if you’re learning data skills and feeling stuck, comment “SQL” and I’ll point you to my resources that I created specifically for beginner data analysts 💻
#Python List Vs Dictionary Reel by @ksk_data - For daily career concepts🧑‍💻, join my group - link is in the bio. 🔗👇 This series is designed to help students and beginners clearly understand wh
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@ksk_data
For daily career concepts🧑‍💻, join my group — link is in the bio. 🔗👇 This series is designed to help students and beginners clearly understand what a Data Engineer and Data Analyst actually do in real companies. In this series, I explain real-world responsibilities like building data pipelines, collecting data from APIs, cleaning data using SQL, designing fact and dimension tables, handling cloud platforms like BigQuery, and supporting business decision-making through reliable data systems. Instead of only theory, you will learn how data flows inside companies, how marketing and product teams use data, and what practical skills are required to become a successful data professional. Whether you are a student between 18–24, a fresher, or someone planning to switch into the data field, this series will give you structured, industry-level clarity in simple English. Follow this channel to learn real data engineering concepts, career guidance, and practical insights that help you prepare for real projects and job roles in the data industry. [ data engineer, data analyst, data engineering roadmap, SQL for beginners, BigQuery tutorial, data pipeline, ETL process, data warehouse, cloud computing, marketing analytics, fact and dimension tables, data modeling, API data integration, real world data projects, analytics career, beginner data course, tech career guidance, business intelligence basics ] #DataEngineer #DataAnalytics #SQL #CloudComputing #TechCareers
#Python List Vs Dictionary Reel by @compilers_developer - Headline: Stop overcomplicating your Data journey. 🛑 I see so many people getting stuck in "tutorial hell" because they don't know which skill to le
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@compilers_developer
Headline: Stop overcomplicating your Data journey. 🛑 I see so many people getting stuck in "tutorial hell" because they don't know which skill to learn next. Do you learn SQL before Python? When do you start your portfolio? I’ve mapped it all out for you. 🗺️ This 8-Step Roadmap covers: ✅ Data Foundations & Roles ✅ Statistics for Data Science ✅ Python & SQL Mastery ✅ Data Viz (Matplotlib/Seaborn) ✅ Portfolio Project Strategy How to get it: 1️⃣ Follow me @compilers_developer 2️⃣ Comment "ROADMAP" below I’ll slide the direct link to the PDF into your DMs! 📩 #DataAnalysis #trending #DataScience #CareerTips #LearnPython SQL DataAnalytics CareerChange TechRoadmap
#Python List Vs Dictionary Reel by @arnitly (verified account) - Most Data Scientists spend 80% of their time on janitor work like cleaning data and brute-forcing hyperparameters. AutoGluon helps you by automating t
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@arnitly
Most Data Scientists spend 80% of their time on janitor work like cleaning data and brute-forcing hyperparameters. AutoGluon helps you by automating the search space so you can focus on the actual business problem. Here’s why this is a game-change: The “Time Limit” Strategy: You can literally tell AutoGluon, “Find me the best model possible in exactly 60 minutes.” It will budget its own training and stacking to ensure you have a result by your next meeting. Model Distillation: Big stacked ensembles are great for accuracy but can be slow in production. AutoGluon can “distill” that massive ensemble back into a single, fast model (like a small ResNet or MLP) that retains most of the accuracy but runs 10x faster. Multimodal Support: It’s not just for CSVs. It can fuse Image + Text + Tabular data into a single Multimodal-Predictor without you having to build complex multi-input architectures by hand. If you’re still manually grid-searching your way through a project, you’re leaving performance on the table. Comment “Repo” and I’ll DM you the link to the official GitHub so you can try it today. #machinelearning #ai #artificialintelligence #Python #algorithm
#Python List Vs Dictionary Reel by @devopspal - Master the Language of Data Science! Ever wondered how an AI actually "learns"? It all starts with two fundamental building blocks: Features and Label
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@devopspal
Master the Language of Data Science! Ever wondered how an AI actually “learns”? It all starts with two fundamental building blocks: Features and Labels. 📊 Think of it like teaching a child to identify fruit: 🔹 Features (X) are the characteristics: The color is red, the shape is round, the texture is smooth. 🔸 Labels (y) is the answer: It’s an “Apple.” In the world of Machine Learning: ✅ Features are your inputs—the data points the model looks at to find patterns (like age, income, or pixels). ✅ Labels are your targets—the specific outcome you want the model to predict (like “Spam” or “Not Spam”). When we train a model, we show it the Features and the Labels together so it can learn the relationship between them. Once trained, we give it new Features, and it predicts the Label for us! 🚀 Swipe left to save this cheat sheet for your next project! 💾 __________________________________________________ Checklist for Success: 📍 Quality over Quantity: Better features lead to better predictions. 📍 Clean your Data: Noisy labels lead to a confused model. 📍 Stay Ethical: Always check your datasets for bias! Which part of the ML pipeline do you find most challenging? Let’s discuss in the comments! 👇 DataScience #MachineLearning #AI #BigData #TechEducation #Python Coding ArtificialIntelligence DataAnalytics LearnAI STEM TechTips

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