AI Tools For Data Analysis

When working with Agentic AI, selecting the right framework is crucial. Each one brings different strengths depending on your project needs — from modular agent designs to large-scale enterprise security. Here's a structured breakdown: ➔ 𝗔𝗗𝗞 (𝗚𝗼𝗼𝗴𝗹𝗲) • Features: Flexible, modular framework for AI agents with Gemini support • Advantages: Rich tool ecosystem, flexible orchestration • Applications: Conversational AI, complex autonomous systems ➔ 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 • Features: Stateful workflows, graph-based execution, human-in-the-loop • Advantages: Dynamic workflows, complex stateful AI, enhanced traceability • Applications: Interactive storytelling, decision-making systems ➔ 𝗖𝗿𝗲𝘄𝗔𝗜 • Features: Role-based agents, dynamic task planning, conflict resolution • Advantages: Scalable teams, collaborative AI, decision optimization • Applications: Project simulations, business strategy, healthcare coordination ➔ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗞𝗲𝗿𝗻𝗲𝗹 • Features: AI SDK integration, security, memory & embeddings • Advantages: Enterprise-grade security, scalable architecture • Applications: Enterprise apps, workflow automation ➔ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗔𝘂𝘁𝗼𝗚𝗲𝗻 • Features: Multi-agent conversations, context management, custom roles • Advantages: Simplifies multi-agent orchestration, robust error handling • Applications: Advanced chatbots, task planning, AI research ➔ 𝗦𝗺𝗼𝗹𝗔𝗴𝗲𝗻𝘁𝘀 • Features: Lightweight, modular multi-agent framework • Advantages: Low-compute overhead, seamless integration • Applications: Research assistants, data analysis, AI workflows ➔ 𝗔𝘂𝘁𝗼𝗚𝗣𝗧 • Features: Goal-oriented task execution, adaptive learning • Advantages: Self-improving, scalable, minimal human intervention • Applications: Content creation, task automation, predictive analysis    Choosing the right Agentic AI framework is less about the "most powerful" and more about 𝗺𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸’𝘀 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁'𝘀 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆, 𝘀𝗰𝗮𝗹𝗲, 𝗮𝗻𝗱 𝗴𝗼𝗮𝗹𝘀. → Which one have you used or are excited to try? → Did I miss any emerging frameworks that deserve attention?

The saying "more data beats clever algorithms" is not always so. In new research from Amazon, we show that using AI can turn this apparent truism on its head. Anomaly detection and localization is a crucial technology in identifying and pinpointing irregularities within datasets or images, serving as a cornerstone for ensuring quality and safety in various sectors, including manufacturing and healthcare. Finding them quickly, reliably, at scale matters, so automation is key. The challenge is that anomalies - by definition! - are usually rare and hard to detect - making it hard to gather enough data to train a model to find them automatically. Using AI, Amazon has developed a new method to significantly enhance anomaly detection and localization in images, which not only addresses the challenges of data scarcity and diversity but also sets a new benchmark in utilizing generative AI for augmenting datasets. Here's how it works... 1️⃣ Data Collection: The process starts by gathering existing images of products to serve as a base for learning. 2️⃣ Image Generation: Using diffusion models, the AI creates new images that include potential defects or variations not present in the original dataset. 3️⃣ Training: The AI is trained on both the original and generated images, learning to identify what constitutes a "normal" versus an anomalous one. 4️⃣ Anomaly Detection: Once trained, the AI can analyze new images, detecting and localizing anomalies with enhanced accuracy, thanks to the diverse examples it learned from. The results are encouraging, and show that 'big' quantities of data can be less important than high quality, diverse data when building autonomous systems. Nice work from the Amazon science team. The full paper is linked below. #genai #ai #amazon

AI holds incredible promise and potential, but something I find especially inspiring is how it can help us solve some of the biggest environmental challenges we face today, such as the global wildfires.    In Canada, Microsoft is working with the Government of Alberta and AltaML on a new AI tool which leverages machine learning to predict the risk of new wildfires by region and even by hour. Capable of analyzing tens of thousands of data points, it provides insights that help firefighting agencies plan ahead, allocate resources efficiently, and prevent fires from spreading out of control – and it’s showing great promise for other wildfire-prone regions around the world.     Learn more here: https://aka.ms/AAmlsim 

New paper – A foundation model for the Earth system Abstract “Reliable forecasting of the Earth system is essential for mitigating natural disasters and supporting human progress. Traditional numerical models, although powerful, are extremely computationally expensive. Recent advances in artificial intelligence (#AI) have shown promise in improving both predictive performance and efficiency, yet their potential remains underexplored in many Earth system domains. Here we introduce Aurora, a large-scale foundation model trained on more than one million hours of diverse geophysical data. Aurora outperforms operational forecasts in predicting air quality, ocean waves, tropical cyclone tracks and high-resolution #weather, all at orders of magnitude lower computational cost. With the ability to be fine-tuned for diverse applications at modest expense, Aurora represents a notable step towards democratizing accurate and efficient Earth system predictions. These results highlight the transformative potential of AI in environmental forecasting and pave the way for broader accessibility to high-quality #climate and #weather information.” Bodnar, C., Bruinsma, W.P., Lucic, A. et al. A foundation model for the Earth system. Nature 641, 1180–1187 (2025). https://lnkd.in/eh8wQ2wx

At first glance, most AI tools feel the same. But choosing the right one can save you hours every week. Here’s my quick guide to where each shines: ⸻ 1. Gemini – Google • Reads and analyzes millions of words without slowing down • Native multimodal — mix text, images, audio, and code in one query • Built into Docs, Sheets, Gmail, and Meet Best for: Teams in Google Workspace needing deep analysis and instant integration 2. Claude – Anthropic • Writes in your tone. Ideal for ghostwriting and thought leadership • Handles complex coding with step-by-step clarity • Turns messy research into concise briefs Best for: Professionals who want an AI collaborator, not just a tool 3. Perplexity AI – Perplexity • Every claim comes with a verifiable source • Academic filter for peer-reviewed research • Instant answers without sign-up Best for: Researchers, students, and analysts who value speed and trust 4. ChatGPT – OpenAI • Largest plugin marketplace for custom tasks • Memory for personalized responses over time • GPT5 reasoning model for advanced problem-solving Best for: Power users needing a creative, analytical “Swiss Army knife” 5. Meta AI – Meta • Free in WhatsApp, Instagram, and Messenger • Open-source base for custom development • Generates images with simple text prompts Best for: Everyday users and small teams who want AI inside familiar apps 6. Grok – xAI • Reads X (Twitter) in real time for trending topics • Witty, sometimes provocative tone that sparks creativity • Bundled with X Premium+ Best for: Marketers, creators, and trend-watchers riding live conversation ⸻ Which AI has been the most useful in your workflow? I’d love to hear how your experience matches or challenges this list. #AI #Productivity #Career

A poor demand forecast destroys profits and cash. This infographic shows 7 forecasting techniques, pros, cons, & when to use: 1️⃣ Moving Average ↳ Averages historical demand over a specified period to smooth out trends ↳ Pros: simple to calculate and understand  ↳ Cons: lag effect; may not respond well to rapid changes ↳ When: short-term forecasting where trends are relatively stable 2️⃣ Exponential Smoothing ↳ Weights recent demand more heavily than older data ↳ Pros: responds faster to recent changes; easy to implement ↳ Cons: requires selection of a smoothing constant ↳ When: when recent data is more relevant than older data 3️⃣ Triple Exponential Smoothing  ↳ Adds components for trend & seasonality ↳ Pros: handles data with both trend and seasonal patterns ↳ Cons: requires careful parameter tuning ↳ When: when data has both trend and seasonal variations 4️⃣ Linear Regression ↳ Models the relationship between dependent and independent variables ↳ Pros: provides a clear mathematical relationship ↳ Cons: assumes a linear relationship ↳ When: when the relationship between variables is linear 5️⃣ ARIMA ↳ Combines autoregression, differencing, and moving averages ↳ Pros: versatile; handles a variety of time series data patterns ↳ Cons: complex; requires parameter tuning and expertise ↳ When: when data exhibits autocorrelation and non-stationarity 6️⃣ Delphi Method ↳ Expert consensus is gathered and refined through multiple rounds ↳ Pros: leverages expert knowledge; useful for long-term forecasting ↳ Cons: time-consuming; subjective and may introduce bias ↳ When: historical data is limited or unavailable, low predictability 7️⃣ Neural Networks ↳ Uses AI to model complex relationships in data ↳ Pros: can capture nonlinear relationships; adaptive and flexible ↳ Cons: requires large data sets; can be a "black box" with less interpretability ↳ When: for complex, non-linear data patterns and large data sets Any others to add?

If I were leveling up as a data analyst right now, I’d focus on these 5 areas (that are actually changing our field with AI) 1. 𝐀𝐈-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 → Use AI tools to detect anomalies, missing values, and outliers faster → Learn prompt-based data profiling to speed up EDA → Automate data transformation scripts with LLMs 📘 Resource: Introducing AI-driven BigQuery data preparation 𝐋𝐢𝐧𝐤: https://lnkd.in/d2W7D_Qt 2. 𝐒𝐦𝐚𝐫𝐭 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 → Use AI to generate dynamic narratives and summaries alongside charts → Explore tools that auto-suggest the best chart for your data → Learn how to build “ask-your-data” interfaces using embedded LLMs 🎓 Resource: Building Python Dashboards with ChatGPT (DataCamp Code Along) 𝐋𝐢𝐧𝐤: https://lnkd.in/dZinchP9 3. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 & 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 → Go beyond trends — learn time series modeling with AI support → Combine traditional models with AI-powered forecasts → Use AI to simulate what-if scenarios from business questions 📘 Resource: Practical Time Series Analysis by Aileen Nielsen (Book) 𝐋𝐢𝐧𝐤: https://lnkd.in/dUVkx4Gx 4. 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐀𝐈 𝐇𝐞𝐥𝐩 → Use AI copilots for writing/debugging complex SQL → Learn how to validate and optimize joins, filters, and aggregations with AI → Automate SQL documentation and data lineage tracking 🎓 Resource: DB-GPT: AI Native Data App Development Framework 𝐋𝐢𝐧𝐤: https://lnkd.in/dc_SpmM6 5. 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐒𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐀𝐈 → Practice generating insights in plain English from data tables → Learn how to convert raw metrics into executive summaries using LLMs → Build dashboards with auto-generated explanations for decision-makers 📘 Resource: Storytelling with Data by Cole Nussbaumer Knaflic (Book) 𝐋𝐢𝐧𝐤: https://lnkd.in/dhD6ZDgJ AI won’t replace your thinking, it will amplify it. Use it to automate the repetitive, and double down on the business impact only you can create. ♻️ Save it for later or share it with someone who might find it helpful! 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 12,000+ readers here → https://lnkd.in/dUfe4Ac6

🌍 Excited to share our new research published in Environmental Science & Technology (ES&T), a journal with an impact factor of 10.9. Our paper introduces Parakeet, an AI solution that combines large language models with semantic matching to automatically recommend emission factors for life cycle assessments - a critical but time-consuming step in carbon footprint calculations. 📊 Organizations often struggle with inconsistent manual mapping processes that can take weeks of expert time and lack clear documentation for audits. Our algorithm achieves 87% accuracy in fully automated matching and 93% accuracy with human review, while providing transparent, verifiable justifications for its recommendations. 🤖 This development significantly accelerates carbon accounting, especially for complex Scope 3 emissions calculations across supply chains. By streamlining this process, we're enabling organizations of all sizes to more efficiently measure and manage their environmental impact as they work toward net-zero emissions targets. This research represents a major step forward in scaling up carbon footprint assessments across industries. 👥 Work with my wonderful colleagues at Amazon: Fahimeh Ebrahimi, Ph.D. Nina Domingo Gargeya Vunnava Abu-Zaher F. Somasundari Ramalingam Shikha Gupta Anran Wang Harsh Gupta Domenic Belcastro Kellen Axten Jeremie Hakian Jared Kramer Aravind Srinivasan and Qingshi Tu, PhD 📄 Link to paper (open access for a limited time, requires sign in): https://lnkd.in/g4fYzdFb 📄 Open link to previous version of paper: https://lnkd.in/dUtva7Nh #amazonscience #sustainability #carbonfootprint #ai

You might have seen news from our Google DeepMind colleagues lately on GenCast, which is changing the game of weather forecasting by building state-of-the-art weather models using AI. Some of our teams started to wonder – can we apply similar techniques to the notoriously compute-intensive challenge of climate modeling? General circulation models (GCMs) are a critical part of climate modeling, focused on the physical aspects of the climate system, such as temperature, pressure, wind, and ocean currents. Traditional GCMs, while powerful, can struggle with precipitation – and our teams wanted to see if AI could help. Our team released a paper and data on our AI-based GCM, building on our Nature paper from last year - specifically, now predicting precipitation with greater accuracy than prior state of the art. The new paper on NeuralGCM introduces 𝗺𝗼𝗱𝗲𝗹𝘀 𝘁𝗵𝗮𝘁 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗱𝗮𝘁𝗮 𝘁𝗼 𝗽𝗿𝗼𝗱𝘂𝗰𝗲 𝗺𝗼𝗿𝗲 𝗿𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 𝗿𝗮𝗶𝗻 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀. Kudos to Janni Yuval, Ian Langmore, Dmitrii Kochkov, and Stephan Hoyer! Here's why this is a big deal: 𝗟𝗲𝘀𝘀 𝗕𝗶𝗮𝘀, 𝗠𝗼𝗿𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: These new models have less bias, meaning they align more closely with actual observations – and we see this both for forecasts up to 15 days, and also for 20-year projections (in which sea surface temperatures and sea ice were fixed at historical values, since we don’t yet have an ocean model). NeuralGCM forecasts are especially performant around extremes, which are especially important in understanding climate anomalies, and can predict rain patterns throughout the day with better precision. 𝗖𝗼𝗺𝗯𝗶𝗻𝗶𝗻𝗴 𝗔𝗜, 𝗦𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗜𝗺𝗮𝗴𝗲𝗿𝘆, 𝗮𝗻𝗱 𝗣𝗵𝘆𝘀𝗶𝗰𝘀: The model combines a learned physics model with a dynamic differentiable core to leverage both physics and AI methods, with the model trained directly on satellite-based precipitation observations. 𝗢𝗽𝗲𝗻 𝗔𝗰𝗰𝗲𝘀𝘀 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲! This is perhaps the most exciting news! The team has made their pre-trained NeuralGCM model checkpoints (including their awesome new precipitation models) available under a CC BY-SA 4.0 license. Anyone can use and build upon this cutting-edge technology! https://lnkd.in/gfmAx_Ju 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: Accurate predictions of precipitation are crucial for everything from water resource management and flood mitigation to understanding the impacts of climate change on agriculture and ecosystems. Check out the paper to learn more:  https://lnkd.in/geqaNTRP

"𝗝𝘂𝘀𝘁 𝘂𝘀𝗲 𝗖𝗵𝗮𝘁𝗚𝗣𝗧" 𝗶𝘀 𝘁𝗲𝗿𝗿𝗶𝗯𝗹𝗲 𝗮𝗱𝘃𝗶𝗰𝗲. Each LLM has unique superpowers - and costly blindspots. Here's how to choose the perfect model for your specific needs:👇 🎯 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 𝗻𝗼𝘄 1. OpenAI - Models: GPT-3, GPT-3.5, GPT-4 (proprietary) - Strengths: Advanced conversational dialogue, multi-step reasoning, efficient computation, real-time interactions. - Weaknesses: Requires commercial license or subscription for full functionality. 2. Anthropic - Models: Claude 3.5 (Proprietary) - Strengths: Incredible contextual understanding, human-like interactions, strong coding capabilities. - Weaknesses: Credit-based subscription service with higher costs for enterprise plans. 3. Google - Models: Gemini (proprietary) - Strengths (Gemini): Large context windows, improved speed, reasoning, and multimodal processing. - Weaknesses: Closed-source and potential data privacy concerns. 4. DeepSeek - Models: DeepSeek-R1 (open-source) - Strengths: Cost-efficient, fast processing speed, superior performance in complex tasks, integrate with proprietary enterprise data. - Weaknesses: Lesser-known compared to other open-source alternatives. 5. Meta - Models: LLaMA (open-source) - Strengths: Multimodal capabilities, improved context window and architecture, competitive performance. - Weaknesses: May require more computational resources for deployment. 6. Mistral AI - Models: Mistral Small 3 (open-source under Apache 2.0 license) - Strengths: Latency-optimized, easily deployable, suitable for low-resource hardware. - Weaknesses: Relatively smaller parameter count compared to other open-source models. 7. Alibaba - Models: Qwen2.5-Max (open-source) - Strengths: Enhanced performance for large-scale natural language processing, low latency, high efficiency. - Weaknesses: Details about parameters and token window size are not publicly disclosed. 🔍 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝗹𝗶𝗺𝗶𝘁𝗮𝘁𝗶𝗼𝗻𝘀 - Cost considerations - Data privacy concerns - Resource requirements - Access Restrictions - Fine-tuning capabilities 💡 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰𝗮𝗹𝗹𝘆 - API integration planning - Scalability assessment - Responsible AI - Performance monitoring - Value Tracking - Cost optimization Don't let your AI strategy fail because of poor model selection. What do you think? ---- 🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX #AI #innovation #technology #automation