#Raw Data

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#Raw Data Reel by @leeinvests (verified account) - It's not in your head. You are statistically poorer than your parents. 🇬🇧📉

We are told to "stop buying coffee" and "work harder."
But look at the — It’s not in your head. You are statistically poorer than your parents. 🇬🇧📉 We are told to “stop buying coffee” and “work harder.” But look at the raw data from 1999 to 2026. In 1999, the average house cost 3.7x the average salary. In 2026, it costs 7.5x the average salary. Your biggest monthly expense (Housing) has grown twice as fast as your paycheck. Your Energy bill has grown 4x faster than your paycheck. The bridge between “Income” and “Living” has collapsed. You cannot out-budget an economy where expenses rise 300-400% while wages rise 95%. The only way to close the gap is to stop playing the “Salary Game” and start playing the “Asset Game.” Inflation destroys savers, but it enriches asset owners. I’m Lee. I’m currently developing an abandoned school onto 13 flats (£3.6M development) @virtusgroupuk and building the UK’s first property software that will find profitable deals for you in 60 seconds @propertyportal.ai. Drop me a DM to get involved or comment WEALTH to learn how we hedge against this mess. This is not financial advice, this is just an illustration, please do your own research.

#Raw Data Reel by @codewithpeterandstewie - Most people only use Task Manager… but it doesn't always show the full truth. If your computer feels slow even when everything looks normal, there's a — Most people only use Task Manager… but it doesn’t always show the full truth. If your computer feels slow even when everything looks normal, there’s a hidden Windows tool that shows the real disk activity. Once you see the raw data, you’ll understand what’s actually happening inside your system. Follow for more hidden tech tricks 🖥️ #TechTips #WindowsTips #PCTricks #HiddenFeatures #ComputerTips

#Raw Data Reel by @mind_vault_hub - Ever noticed how our brain doesn't always show us reality, but instead shows us what it thinks is right? 🧠✨
This video is a perfect example of how po — Ever noticed how our brain doesn’t always show us reality, but instead shows us what it thinks is right? 🧠✨ This video is a perfect example of how powerful — and easily fooled — the human mind can be. At first glance, one object looks bigger and the other looks smaller. But here’s the twist: both objects are actually the same size. The illusion is created using simple graph lines, perspective, and distance cues. Our brain automatically interprets these lines as depth, making one object appear farther away. And when something seems farther, the brain assumes it must be bigger to appear the same size — even when it’s not. This phenomenon is a classic example of how our visual system relies on past experiences and assumptions rather than raw data. In real life, parallel lines often represent roads, railways, or paths moving into the distance. So when the brain sees similar patterns here, it applies the same logic — without questioning it. What’s fascinating is that even after knowing the truth, the illusion doesn’t disappear. You know they’re the same size, yet your eyes still disagree. That’s because perception isn’t controlled by logic — it’s controlled by interpretation. This illusion reminds us of something deeper: Just like vision, our thoughts, opinions, and judgments are also shaped by perspective. Two people can look at the same situation and see completely different realities — not because one is wrong, but because their minds are interpreting the information differently. So next time something looks bigger, smaller, better, or worse — pause for a moment. Ask yourself: Is this reality… or just my brain filling in the gaps? 🧠 Perception is powerful. 🎯 Perspective shapes reality. 👀 Not everything is what it seems. #MindIllusion #BrainTricks #OpticalIllusion #PsychologyFacts #Neuroscience HumanBrain VisualIllusion Perception MindBlown ScienceBehindIt PerspectiveMatters RealityVsPerception

#Raw Data Reel by @penelope_data - 9 data projects ideas instead of doomscrolling:

(And there is a repo with the data :) )

🤖 Machine Learning Projects
* Diabetes Classification Build — 9 data projects ideas instead of doomscrolling: (And there is a repo with the data :) ) 🤖 Machine Learning Projects * Diabetes Classification Build and compare classification models to show how data preprocessing, feature scaling, and hyperparameter tuning directly improve predictive performance. * Heart Attack Prediction Implement an end-to-end classification pipeline—from raw data to model evaluation—to demonstrate a realistic machine learning workflow. * Medical Cost Prediction Train a regression model to predict healthcare costs, emphasizing feature importance analysis and model optimization to explain what drives predictions. 🛠️ Data Engineering Projects * NBA Player Statistics ETL Pipeline Design an ETL pipeline that extracts player statistics, cleans and transforms the data, and stores it in a relational database for reliable downstream analysis. * Real-Time & Batch Data Pipelines with Kafka Build a scalable pipeline that processes streaming and batch data using Kafka, PostgreSQL, and Docker to demonstrate modern data flow architecture. * Glassdoor Job Data Pipeline Scrape job postings, clean and structure the raw data, and prepare it for analysis and visualization to showcase real-world data ingestion challenges. 📊 Data Analytics Projects * Pokémon Dataset Analysis Perform exploratory data analysis and feature engineering to uncover patterns in Pokémon characteristics such as types, stats, and legendary status. * Automated EDA Tool Comparison Benchmark AutoViz, SweetViz, and Pandas Profiling across multiple datasets to evaluate performance, resource usage, and practical trade-offs. * Exploratory Job Market Analysis Analyze cleaned job posting data to extract trends, key skills, and role distributions using visualizations and summary statistics. 👉🏻 Comment « data » to get the link to the repo and portfolio strategies! #data #students #job

#Raw Data Reel by @jayenthakker - A business leader once told me:
"𝘑𝘢𝘺𝘦𝘯, 𝘸𝘦 𝘩𝘢𝘷𝘦 𝘵𝘰𝘯𝘴 𝘰𝘧 𝘥𝘢𝘵𝘢… 𝘣𝘶𝘵 𝘯𝘰 𝘳𝘦𝘢𝘭 𝘷𝘪𝘴𝘪𝘣𝘪𝘭𝘪𝘵𝘺."

Truth is, most #leade — A business leader once told me: "𝘑𝘢𝘺𝘦𝘯, 𝘸𝘦 𝘩𝘢𝘷𝘦 𝘵𝘰𝘯𝘴 𝘰𝘧 𝘥𝘢𝘵𝘢… 𝘣𝘶𝘵 𝘯𝘰 𝘳𝘦𝘢𝘭 𝘷𝘪𝘴𝘪𝘣𝘪𝘭𝘪𝘵𝘺." Truth is, most #leaders aren’t short on data. They’re short on clarity. That’s why the right #dashboards aren’t “nice to have”, they’re 𝐧𝐨𝐧-𝐧𝐞𝐠𝐨𝐭𝐢𝐚𝐛𝐥𝐞. Here are 𝟓 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 every business leader should be using (and why): 1. Sales Performance Dashboard ⤷ Track revenue, pipeline, targets vs actuals, top-performing reps. ⤷ Because “gut feel” doesn’t close deals—data does. 2. Customer Insights Dashboard ⤷ Understand who your customers are, what they love, and where they churn. ⤷ Because retention > acquisition. 3. Financial Health Dashboard ⤷ Monitor cash flow, margins, costs, and profitability in real-time. ⤷ Because what you don’t measure… leaks. 4. Marketing Campaign Dashboard ⤷ See what’s working, what’s wasting money, and what’s driving #ROI. ⤷ Because every marketing rupee should tell a story. 5. Operations & Supply Chain Dashboard ⤷ Keep an eye on delivery timelines, inventory, and vendor performance. ⤷ Because delays don’t just cost time—they cost trust. If you're a #business leader relying only on reports… You're already behind. 𝘋𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥𝘴 𝘵𝘶𝘳𝘯 𝘴𝘤𝘢𝘵𝘵𝘦𝘳𝘦𝘥 𝘥𝘢𝘵𝘢 𝘪𝘯𝘵𝘰 𝘥𝘢𝘪𝘭𝘺 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘴. Which of these dashboards do you rely on the most—or wish you had? Let’s talk 👇 #dataanalytics #datavisualization #datascience #sql #artificialintelligence

#Raw Data Reel by @wealthymentors_ - In Silicon Valley, this scene shows Richard's way of breaking down howehis compression algorithm works. He shows two grids of dots -one full and unorg — In Silicon Valley, this scene shows Richard's way of breaking down howehis compression algorithm works. He shows two grids of dots -one full and unorganized, representing raw data, and another with the same information but packed tightly together. The point is simple: his system takes something massive and arranges it so efficiently that the size shrinks dramatically, but without losing any of the original pieces. Every dot in the compressed version can be mapped back to where it came from, proving that the process isn't just about saving space, it's about doing it without sacrificing accuracy. This demonstration explains why his compression is such a breakthrough. Traditional methods often mean trade-offs-you either reduce file size and lose some quality, or you keep quality but can't shrink it enough. Richard's middle-out approach changes that. By reorganizing data at a deeper level, his algorithm delivers both smaller size and perfect reconstruction. The dots become more than a visual aid; they show how his technology could reshape storage, streaming, and even encryption, making massive data usable in ways that weren't possible before. Follow @wealthymentors_ to learn daily entrepreneurial lessons #tech #ai #data #siliconvalley #explore

#Raw Data Reel by @sdw.online (verified account) - 5 places to find real datasets the top 1% use for their portfolios:

1. OpenNeuro
2. NYC Open Data
3. Socrata Open Data
4. World Bank Open Data
5. S — 5 places to find real datasets the top 1% use for their portfolios: 1. OpenNeuro 2. NYC Open Data 3. Socrata Open Data 4. World Bank Open Data 5. Securities and Exchange Commission (SEC) What else belongs on this list?

#Raw Data Reel by @mar_antaya (verified account) - Using real world data is always best for when building and learning because data is THE most important part of your model!!!! #data — Using real world data is always best for when building and learning because data is THE most important part of your model!!!! #data

#Raw Data Reel by @hustleuphoney - Day 1 of Learning PySpark/Spark

Before jumping into PySpark, it's important to understand how Big Data was processed earlier.
Earlier, we used Hadoop — Day 1 of Learning PySpark/Spark Before jumping into PySpark, it’s important to understand how Big Data was processed earlier. Earlier, we used Hadoop MapReduce to process large amounts of data. It works in two main steps: •Map Phase Raw data is processed and converted into key-value pairs (intermediate results) •Reduce Phase All the intermediate results are combined to produce the final output Example (Easy to Understand) Suppose you have sales data like this: Mumbai → 500 Delhi → 300 Mumbai → 200 Delhi → 400 Mumbai → 100 In the Map phase, data is simply processed and grouped like: Mumbai → 500, 200, 100 Delhi → 300, 400 In the Reduce phase, values are added: Mumbai → 800 Delhi → 700 But here’s the problem 👉 All these intermediate results are stored on disk (not memory) 👉 Every step involves writing to disk and reading again 👉 This creates too many disk I/O operations Because of this, processing becomes slow and inefficient, especially when working with huge data (GBs/TBs). 🐢 This limitation is exactly why a better solution was needed… And that’s where Apache Spark comes in Next: How Spark solves this problem and makes processing faster

#Raw Data Reel by @nijatchats (verified account) - Data analysis isn't about crunching numbers anymore 🤯 Comment "docs" for a list of prompts and a guide around data analysis with AI.
Mastering AI fo — Data analysis isn't about crunching numbers anymore 🤯 Comment "docs" for a list of prompts and a guide around data analysis with AI. Mastering AI for data visualization is less about being a technician and more about becoming a strategic storyteller. This single shift in focus is what separates junior analysts from senior leaders.

#Raw Data Reel by @dataposts - Follow @dataposts for more. #data #stats #statistics #datascience #dataanalytics #datascientist #statistics #information #timeline — Follow @dataposts for more. #data #stats #statistics #datascience #dataanalytics #datascientist #statistics #information #timeline

#Raw Data Reel by @dataanalystduo (verified account) - Do dashboards alone get you shortlisted?

In most cases, no.

Watch the entire reel to understand.

#dataanalytics #datascience #dataanalystduo #da — Do dashboards alone get you shortlisted? In most cases, no. Watch the entire reel to understand. #dataanalytics #datascience #dataanalystduo #dataanalyst #data

✨ #Raw Data発見ガイド

Instagramには#Raw Dataの下に15K件の投稿があり、プラットフォームで最も活気のあるビジュアルエコシステムの1つを作り出しています。

Instagramの膨大な#Raw Dataコレクションには、今日最も魅力的な動画が掲載されています。@dataposts, @leeinvests and @jayenthakkerや他のクリエイティブなプロデューサーからのコンテンツは、世界中で15K件の投稿に達しました。

#Raw Dataで何がトレンドですか？最も視聴されたReels動画とバイラルコンテンツが上部に掲載されています。

人気カテゴリー

📹 ビデオトレンド: 最新のReelsとバイラル動画を発見

📈 ハッシュタグ戦略: コンテンツのトレンドハッシュタグオプションを探索

🌟 注目のクリエイター: @dataposts, @leeinvests, @jayenthakkerなどがコミュニティをリード

#Raw Dataについてのよくある質問

Pictameを使用すれば、Instagramにログインせずに#Raw Dataのすべてのリールと動画を閲覧できます。あなたの視聴活動は完全にプライベートです。ハッシュタグを検索して、トレンドコンテンツをすぐに探索開始できます。

パフォーマンス分析

12リールの分析

✅ 中程度の競争

💡 トップ投稿は平均1.5M回の再生（平均の2.7倍）

週3-5回、活動時間に定期的に投稿

コンテンツ作成のヒントと戦略

💡 トップコンテンツは10K以上再生回数を獲得 - 最初の3秒に集中

✨ 多くの認証済みクリエイターが活動中（42%） - コンテンツスタイルを研究

📹 #Raw Dataには高品質な縦型動画（9:16）が最適 - 良い照明とクリアな音声を使用

✍️ ストーリー性のある詳細なキャプションが効果的 - 平均長853文字

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📈戦略探求者向け

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