#Data Analytics Tutorial

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#Data Analytics Tutorial Reel by @jessramosdata (verified account) - My @linkedin analytics report hits my Slack every Monday before I even touch my coffee 💅🏻 comment "data" for full YouTube tutorial in only 6 min H
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@jessramosdata
My @linkedin analytics report hits my Slack every Monday before I even touch my coffee 💅🏻 comment “data” for full YouTube tutorial in only 6 min Here’s how I built my Data Analyst AI Agent (NO CODE!) ⬇️ ↳ Setup your agent in Zapier: Start a new agent using Copilot and describe the workflow you want in plain language. Let it build for you. ↳ Configure your trigger: I set mine to run every Monday at 9 AM, then connected it to my LinkedIn data source (Google Sheet) ↳ Write your prompt: Specify the time period, think of what keeps your insight fresh. Define your KPIs and add variables to track (asset type, hook, links, topic). Ask for an executive summary at the end ↳ Set your actions: Connect your Slack channel and Gmail to receive the full report automatically ↳ Before you go live: Keep the human in the loop by running a test. NEVER give AI full autonomy without guardrails in place
#Data Analytics Tutorial Reel by @ai_herway - AI agents are everywhere right now. And so are the tutorials. 🫣🤯 Before you download a single one - I want you to ask one question. ➡️ How long ha
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@ai_herway
AI agents are everywhere right now. And so are the tutorials. 🫣🤯 Before you download a single one - I want you to ask one question. ➡️ How long has this person actually been doing this? Because the space is flooded with people who figured out agents six months ago, got excited, and immediately started teaching. The energy is real. The enthusiasm is genuine. But enthusiasm is not the same as expertise. ❌ The things that matter most in agent setup - data jurisdiction, scoped access, what happens when something misfires into a client relationship - these don’t show up in the first six months. They show up later. When it’s already too late to undo. I have a PhD. Nearly two decades in data science. I’ve been building agents with Claude Code, Make, and n8n for years - since before most people knew the word existed. I’ve advised governments on AI governance. And I still made mistakes when I built mine. The difference is I knew exactly what they were. You don’t need the loudest person in the internet selling you a tutorial. You need the most rigorous one. Comment COO and I’ll send you the guide. 👇
#Data Analytics Tutorial Reel by @askdatadawn (verified account) - Comment "AI" and I'll send you my roadmap for Data Scientists to upskill in AI Engineering 🧠 AI is quickly becoming a non-negotiable part of any Dat
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@askdatadawn
Comment “AI” and I’ll send you my roadmap for Data Scientists to upskill in AI Engineering 🧠 AI is quickly becoming a non-negotiable part of any Data Scientists job. Whether it be using AI tools to be more efficient, building AI workflows, or even fine-tuning LLM models. The great news is that Data Scientists are perfectly position to upskill into AI engineering because we already have the foundations of statistics and machine learning. The future is coming. It’s coming fast. But you got this. You can evolve with the times 💪🏽 #datascience #aiengineering
#Data Analytics Tutorial Reel by @techviz_thedatascienceguy (verified account) - Difference between LLMs and Agents 🚀 - Large Language Models (LLMs) are AI systems trained on huge amounts of text data to understand and generate
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@techviz_thedatascienceguy
Difference between LLMs and Agents 🚀 — Large Language Models (LLMs) are AI systems trained on huge amounts of text data to understand and generate human-like language. They can answer questions, write content, explain concepts, and even help with coding — just like a smart assistant. — AI Agents take this a step further. Instead of just responding, agents can plan, take actions, and complete tasks on their own. They use LLMs as their brain, connect with tools (like APIs, databases, or apps), and work step-by-step to achieve a goal. 🫱 Follow @techviz_thedatascienceguy for more! 🏷️ LLM, AI Agents, Artificial Intelligence, Machine Learning, Generative AI, AI Explained, Data Science, AI Trends, Tech Explained, Future of AI, AI concept explained, ML concept explained, AI interview, Data Interview Questions #ai #aicontent #llms #agenticai #datasciencejobs
#Data Analytics Tutorial Reel by @themadhurmehta (verified account) - 5 AI TOOLS YOU NEED IN 2026 (Part 2) If your goal is to automate your workflow from data analysis to content creation, these are the 5 tools that act
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@themadhurmehta
5 AI TOOLS YOU NEED IN 2026 (Part 2) If your goal is to automate your workflow from data analysis to content creation, these are the 5 tools that actually matter. 1️⃣ Julius AI: If you want to analyze complex data and create charts, use this. 2️⃣ Runway ML: If you want to generate cinematic videos from text, use this. 3️⃣ Eraser AI: If you want to generate system architecture diagrams from text, use this. 4️⃣ Mage AI: If you want to build and automate data pipelines, use this. 5️⃣ BigQuery: If you want to run ML models directly on your data with SQL, use this. 📩Save this for later. ♻️Share this with someone to upgrade from tools to agents. 💡Follow for part 3! . . . AI tools, artificial intelligence, automation, productivity hacks, data engineering, cloud computing, tech trends 2026, software engineering, devops, machine learning, data analysis, system design, workflow automation, ai agents, tech stack, innovation
#Data Analytics Tutorial Reel by @sundaskhalidd (verified account) - Part 1: Let's build a real AI Data Analyst from scratch 📈 ㅤ This is Part 1 of my Build AI Agent series where we build practical, working agents using
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@sundaskhalidd
Part 1: Let's build a real AI Data Analyst from scratch 📈 ㅤ This is Part 1 of my Build AI Agent series where we build practical, working agents using Python, LangChain, and OpenAI. ㅤ Comment which agent you want to see next? ㅤ Follow for the next part ㅤ #aiagents #python #openai #dataanalytics
#Data Analytics Tutorial Reel by @datasciencebrain (verified account) - Stop paying for AI courses. Google, Meta, NVIDIA, OpenAI, they publish the same material they teach internally. For free. Forever. Here are 10 platf
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@datasciencebrain
Stop paying for AI courses. Google, Meta, NVIDIA, OpenAI, they publish the same material they teach internally. For free. Forever. Here are 10 platforms where billion-dollar AI companies teach you their stack at zero cost → ↳ Anthropic — anthropic.skilljar.com ↳ Google AI — grow.google/ai ↳ Meta AI — ai.meta.com/resources ↳ NVIDIA Training — developer.nvidia.com/training ↳ Microsoft Learn — learn.microsoft.com/training ↳ OpenAI Docs — platform.openai.com/docs ↳ IBM SkillsBuild — skillsbuild.org ↳ AWS Skill Builder — skillbuilder.aws ↳ DeepLearning.AI — deeplearning.ai ↳ Hugging Face — huggingface.co/learn Bookmark this post. Your future self will thank you. Follow & Comment to get links in DM 📥 📲 Follow @datasciencebrain for Daily Notes 📝, Tips ⚙️ and Interview QA🏆 . . . . . . [dataanalytics, artificialintelligence, deeplearning, bigdata, agenticai, aiagents, statistics, dataanalysis, datavisualization, analytics, datascientist, neuralnetworks, 100daysofcode, llms, datasciencebootcamp, ai] #datascience #dataanalyst #machinelearning #genai #aiengineering
#Data Analytics Tutorial Reel by @sundaskhalidd (verified account) - Which AI tool do you use the most in your day-to-day work? ㅤ #datascience #dataanalytics #techcareers #aiworkflow #careeradvice #aitools
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@sundaskhalidd
Which AI tool do you use the most in your day-to-day work? ㅤ #datascience #dataanalytics #techcareers #aiworkflow #careeradvice #aitools
#Data Analytics Tutorial Reel by @socho.abhi - Comment "AI" to upskill yourself! Most companies now use data + AI to make everyday decisions, and the real gap isn't talent, it's people who know ho
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@socho.abhi
Comment “AI” to upskill yourself! Most companies now use data + AI to make everyday decisions, and the real gap isn’t talent, it’s people who know how to apply these tools to business problems. That’s why programs like BITSoM’s 6-month Business Analytics with Gen & Agentic AI exist. Not to teach theory, but to train people on how companies actually work today — using Excel, SQL, Python, Tableau, and AI tools like ChatGPT on real datasets, real cases, real decisions. The focus is simple: build things you can show, not concepts you memorise. If you’re curious: – Entry is through a ₹99 qualifier test – 60 minutes, no coding – Happening this Sunday – Limited seats [business analytics, genai careers, ai jobs india, data analytics course, bitsom, ai in business, upskilling india, genai skills, mba analytics, ai careers]
#Data Analytics Tutorial Reel by @ignitecircuit - Drop a follow on my main @aicollectiveco #AI #ArtificialIntelligence #AIVideo #AIGenerated #viral #fyp #AITech #FutureTech #MachineLearning #TechTre
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@ignitecircuit
Drop a follow on my main @aicollectiveco #AI #ArtificialIntelligence #AIVideo #AIGenerated #viral #fyp #AITech #FutureTech #MachineLearning #TechTrends #AIReels #AIFuture
#Data Analytics Tutorial Reel by @ignitecircuit - Drop a follow on my main @aicollectiveco #AI #ArtificialIntelligence #AIVideo #AIGenerated #viral #fyp #AITech #FutureTech #MachineLearning #TechTre
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@ignitecircuit
Drop a follow on my main @aicollectiveco #AI #ArtificialIntelligence #AIVideo #AIGenerated #viral #fyp #AITech #FutureTech #MachineLearning #TechTrends #AIReels #AIFuture
#Data Analytics Tutorial Reel by @fellowtechiebuddy - If you don't know these basic AI terminologies, you should NOT apply to AI roles 🚫 Here's what each means: Tokens - Basic units LLMs process (words
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@fellowtechiebuddy
If you don’t know these basic AI terminologies, you should NOT apply to AI roles 🚫 Here’s what each means: Tokens - Basic units LLMs process (words or word pieces) Embeddings - Numbers representing text/data in vector space Context Window - Max text a model can process at once Fine-tuning - Training a pre-trained model on your specific data Prompting - How you structure input to get better outputs Transformers - Architecture behind modern LLMs Attention Mechanism - How models focus on relevant input parts Parameters - Learned weights in the model (billion parameters = model size) Inference - Running the model to get predictions RAG - Retrieval Augmented Generation - fetching data before generating Vector Database - Stores embeddings for fast similarity search Latency - Response time from your model Quantization - Reducing model size by lowering precision Loss Function - Measures how wrong your predictions are Overfitting - Model memorizes training data, fails on new data Temperature - Controls randomness in model outputs RLHF - Training models with human feedback (how ChatGPT learns) Hallucination - When models generate confident but wrong information This is to give you an idea of what each word means but you should learn these in depth. #AIEngineering #MachineLearning #LLM #AI TechCareers SoftwareEngineering​​​​​​​​​​​​​​​​

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