AI Tools For Data Analysis

🚀 AlphaEarth Foundations (AEF) - New from Google DeepMind I keep looking out for interesting usecases of AI. Deepmind folks are at it again. 📄 Paper: AlphaEarth Foundations on arXiv (https://lnkd.in/giHUwe2d) --- 🌍 What is AlphaEarth Foundations? AEF is a foundation model for Earth observation that turns sparse and messy satellite, climate, LiDAR, and even text data into dense embeddings at 10 m² resolution. These embeddings provide a universal feature space for mapping and monitoring the planet, outperforming all previous approaches — reducing mapping errors by ~24% on average. And the best part? The embeddings are already available as annual global datasets (2017–2024) for free: 👉 Earth Engine Data Catalog: Google Satellite Embedding V1 Annual - https://lnkd.in/g6dcv4-M --- 🛠 Why does this matter? (weekend project ?) For places like Bengaluru, India (or any fast-changing city), AEF makes it possible to: - Track urban growth and land use change with very few ground samples. - Monitor lakes and wetlands for encroachment and seasonal changes. - Map flood risk by combining rainfall, elevation, and land cover. - Identify urban heat islands and vegetation loss. - Support peri-urban agriculture with low-shot crop type classification. - Study biodiversity shifts (tree species, invasive plants) by linking with GBIF/iNaturalist data. In short, it’s like having a plug-and-play geospatial backbone — ready to support everything from city planning to climate adaptation. --- 🔧 For the Geeks Want to try it out? You can get started in minutes using Earth Engine + Python: 📘 Earth Engine Python Quickstart Docs - https://lnkd.in/g9zBBPJv 🌐 This is a big step toward planetary-scale AI for environmental monitoring — making high-quality maps possible even when labels are scarce. --- Further reading : 1. https://lnkd.in/gsXU2BqS 2. https://lnkd.in/gxJpqS6b --- Authors: Christopher Brown, Michal Kazmierski, Valerie Pasquarella, William J. Rucklidge, Masha Samsikova, Chenhui Zhang, Evan Shelhamer, Estefania Lahera, Olivia Wiles, Simon Ilyushchenko, Noel Gorelick, Lihui Lydia Zhang, Sophia Alj, Emily Schechter, Sean Askay, Oliver Guinan, Rebecca Moore, Alexis Boukouvalas, Pushmeet Kohli.

When your deep insight requires a statistical model, using an LLM can be a smart first steps. But a black-box solution is an expensive gamble. A model without diagnostics is a structure without a foundation. Your model will collapse without rigor. Your LLM is not a Data Scientist. It is a tool. Command it to build the framework, not the answer. This prompt sequence gives you control and will help you guide the LLM to produce complex models with the rigor you need and the speed you want. 1. Command Governance: Force the LLM to verify statistical assumptions *before* training. 2. Command Efficiency: Define intelligent, limited tuning to optimize resource allocation. 3. Command Auditability: Demand documentation and model notes be generated *with* the final code. Here's a link to four LLM prompt frameworks that will help you run this sequence: https://bit.ly/3X9LEAh Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

There’s a lot of excitement around using LLMs for forecasting. Fair. But here’s the practical answer: LLMs are not a drop-in replacement for time series models. If the problem is highly numerical, high-frequency, or tightly dependent on temporal structure, classical models still do the heavy lifting better. ARIMA, ETS, LightGBM, Lag features, Rolling statistics.... These are still the workhorses. Where teams get disappointed is when they expect an LLM to do raw forecasting better just because it is powerful. That rarely works. LLMs are not great at strict numerical precision. And they do not naturally respect temporal dependencies the way forecasting models do. The better architecture is a hybrid workflow. Use traditional models for the math. Use LLMs for the context around the math. That’s where things start getting interesting. LLMs can help with 1. Feature engineering from text-heavy signals like news, commentary, or notes 2. Better data representation when time series is paired with structured metadata 3. Contextual reasoning around seasonality, holidays, payday effects, or business events 4. Anomaly interpretation after statistical methods detect something unusual That is the real shift. Not LLMs instead of forecasting. LLMs around forecasting. In text-rich or data-scarce environments, that extra layer can matter. Because numbers tell you what changed. Context tells you why.

Unlocking the Power of GeoAI: From Raw Geospatial Data to Actionable Insights GeoAI is fundamentally changing the way we work with geospatial data. Today, artificial intelligence is not just a research topic, but a practical tool that helps us turn massive amounts of aerial imagery and lidar data into real, actionable information. By combining neural networks with proven photogrammetry and rule-based quality assurance, we can now extract detailed land cover maps, analyze urban surfaces, and even simulate urban climate with a level of precision that was unthinkable just a few years ago. One of the most exciting aspects is how GeoAI enables us to move beyond traditional mapping. With AI-powered segmentation, we can distinguish even the smallest features in urban environments and keep our data up to date. Thanks to TrueOrthos and advanced photogrammetric workflows, geometric distortions are a thing of the past, so data from different times and sensors can be perfectly aligned. This is essential for reliable change detection and multi-source analysis. But the possibilities go even further. Automated analysis of sealed and unsealed surfaces helps cities identify where to prioritize “desealing” for climate resilience. Parcel indexing allows us to aggregate key indicators like green space, building area, or solar installations at any scale, supporting truly data-driven decisions in urban planning and environmental monitoring. And with urban climate simulation, we can combine pixel-precise land cover data with 3D voxel models and CFD to visualize the effects of new trees, green roofs, or lighter pavements, before any construction begins. Even lidar point cloud classification benefits from GeoAI. By combining AI with rule-based checks and external data sources, we achieve robust, scalable, and quality-assured 3D mapping, reducing manual effort and increasing reliability, even in complex or changing environments. GeoAI is already a productive, scalable approach that is shaping the sustainable, data-driven development of our cities and landscapes. With annual updates and hybrid workflows, we ensure that results are not only precise and up to date, but also trusted and actionable. If you want to learn how to turn your geospatial data into valuable information using GeoAI, just reach out or send me a message. Let’s move from data to information, using GeoAI. 💡 Comment | Like | Share   👉 Follow me (Dr. Uwe Bacher) for more Information on exciting topics from the world of geospatial #GeoAI #Geospatial #AerialImagery #Lidar #UrbanPlanning #AI #SmartCities

Google ADK + Zerodha MCP + LLMs: Autonomous portfolio analysis in action. Modern financial analysis is rapidly moving toward automation and agentic workflows. Integrating large language models (LLMs) with real-time financial data unlocks not just powerful insights but also new ways of interacting with portfolio information. This experiment brings together secure browser-based authentication, live data retrieval from Zerodha’s MCP, and LLM-driven risk and performance analytics—all orchestrated autonomously. This is a starter kit to get you going, but it can be extended to support sophisticated, fully automated quantitative models—simply by crafting effective prompts. I've made the experiment available on my GitHub repo. Please feel free to explore or adapt it for your own agentic financial analysis workflows. Code and documentation: https://lnkd.in/gme977GG #agenticai #mcp

Can LLMs generate human-readable hypotheses from data? Interpreting data & coming up with hypotheses to explain it is a key part of scientific research. This paper looks at social sciences datasets, where you only need text to express hypotheses rather than mathematical notation, and investigates whether LLMs are able to generate valid and high quality hypotheses that can explain the data. For example, in a dataset of dishonest reviews, the system was able to generate the hypothesis that reviews mentioning personal experiences or special occasions were more likely to be honest. The datasets included honesty of hotel reviews, and popularity of tweets & headlines. In the proposed approach, LLMs were first used to generate hypotheses when given example datapoints. Then, the LLM was used to make predictions about the full dataset to determine how accurate the hypothesis was & to weed out low quality hypotheses. Using the iterative method, the LLM was able to suggest hypotheses that had already been proposed in existing literature, and also propose several new insights about these social science datasets. #artificialintelligence #largelanguagemodels

🌲 Forests don’t vanish silently… but satellites see it every day. Illegal logging, urban expansion, and climate change are rapidly reducing forest cover. Traditional monitoring is slow, manual, and often incomplete. Here’s how GeoAI + Deep Learning on Google Earth Engine (GEE) can change the game: ✅ Analyze multi-temporal satellite imagery (Landsat / Sentinel-2) ✅ Extract forest features like NDVI, canopy cover, and texture ✅ Train deep learning models (CNN / U-Net / ResNet) to detect forest loss ✅ Automate real-time forest change maps and alerts Outputs: • Forest loss / gain maps (seasonal or yearly) • Hotspot detection of deforestation • Predictive risk maps for forest degradation • Dashboards for decision-makers This isn’t just mapping — it’s actionable intelligence for conservation, policy, and climate resilience. I’m excited to connect with projects and organizations using GeoAI for environmental monitoring and sustainable forest management. #GeoAI #RemoteSensing #DeepLearning #ForestChange #ClimateTech #GEE #SpatialAnalytics #EnvironmentalMonitoring

Structured data like tables and graphs isn't just for spreadsheets anymore! 🚀StructLM proposes a new way of using LLMs to process and interpret structured data sources, outperforming 14 out of 18 benchmark existing models. 📊🔍 Implementation 1️⃣ Created a large dataset focusing on structured data e.g., question-answering, summarization, fact verification on different formats tables, databases, knowledge graphs 2️⃣ Fine-tuned CodeLlama (7-34B) on the dataset with instruction tuning by pairing system prompts with instructions. 3️⃣ Benchmark the models against state-of-the-art task-specific models across a diverse set of tasks. Insights 📊 Dataset includes over 1.1M samples from 25 tasks, including Table QA and fact verification. 🏆 StructLM achieves new state-of-the-art on 7 out of 18 benchmarks. 📈 Performance scales weakly with model size; 34B is only slightly better than 7B, suggesting the challenge of structured data. 💻 Code pretraining is more important than math pertaining. 🌍 Great example for domain adoption, StructLM 7B achieves avg. 71.1% and GPT-3.5 only of 39.5%. 🤗 Models & Datasets available on Hugging Face. Paper: https://lnkd.in/enKNV5mm Github: https://lnkd.in/eg_KCjBR Models & Dataset: https://lnkd.in/eu7DF44v

🌍📡 Introducing My Global RUSLE–AI Toolkit in Google Earth Engine (1985–2024) #OnOneClick you will get your results any where in the world. Thrilled to share my latest research contribution — a fully automated RUSLE (Revised Universal Soil Loss Equation) Soil Erosion Analysis Toolkit, built entirely in Google Earth Engine (GEE) and enhanced with state-of-the-art AI/ML models. This toolkit processes 40 years of satellite data (1985–2024) to generate high-resolution, annual soil erosion maps, factor layers, trends, predictions, and AI-assisted susceptibility modelling. 🚀 What the Toolkit Does ✔ Automates R, K, LS, C, P factor computation for every year (1985–2024) ✔ Integrates multi-sensor data (Landsat, Sentinel, CHIRPS, DEM, LandCover datasets) ✔ Generates annual soil erosion maps, spatial statistics, time-series trends ✔ Performs AI-based soil erosion susceptibility modelling using: 🔹 Support Vector Machine (SVM) 🔹ANN 🔹 RNN 🔹 CNN 🔹 Classification & Regression Tree (CART) 🔹 Random Forest (RF) 🔹 k-Nearest Neighbors (kNN) 🔹 Logistic Regression ✔ Produces class-wise area results, charts, accuracy assessment, ROC/AUC ✔ Provides a single-click interface using GEE UI Panels ✔ Allows global-scale or watershed-scale implementation for any AOI 🧠 Why This Toolkit Matters Soil erosion remains one of the most critical global environmental challenges—impacting: Reservoir siltation Agricultural productivity River morphology Water resource planning Climate resilience By combining Earth Observation, Cloud Computing, and Machine Learning, this toolkit bridges the gap between hydrology, geomorphology, and geospatial AI. 🛰 Data Sources (1985–2024) Landsat 5, 7, & 8 surface reflectance Sentinel-2 MSI CHIRPS rainfall SoilGrids / OpenLandMap SRTM / ASTER DEM MODIS NDVI Global LULC datasets 🛠 Applications 🔹 Soil erosion mapping & monitoring 🔹 Sediment yield estimation for reservoirs 🔹 Climate-driven land degradation studies 🔹 Watershed prioritization 🔹 AI-based erosion risk assessment 🔹 Policy & decision-support systems 🎯 Outcome A scalable, reproducible, and globally deployable toolkit enabling researchers, agencies, and policymakers to monitor and predict soil erosion at unprecedented temporal depth and spatial resolution. 📥 If you want access to the toolkit or want to collaborate, feel free to connect! (+923359435216) Advancing geospatial intelligence for a sustainable and climate-resilient future. 🌱🌍

If you want an example of how AI empowers data scientists, consider this one: A new study shows how we can use LLMs to harness unstructured data and the knowledge embedded in their pre-training to drive significant reduction in variance in experiments (think CUPED, but for multimodal data). 𝐋𝐋𝐌𝐬 𝐥𝐚𝐜𝐤 𝐠𝐮𝐚𝐫𝐚𝐧𝐭𝐞𝐞𝐬 𝐨𝐧 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲, and naively using them to predict counterfactuals isn't statistically valid. Few-shot LLMs rely on a small set of demo examples. This makes predictions variable and correlated across observations, breaking the independence assumption for valid causal analysis. The study shows 𝐡𝐨𝐰 𝐭𝐨 𝐮𝐬𝐞 𝐋𝐋𝐌𝐬 𝐭𝐨 𝐫𝐞𝐝𝐮𝐜𝐞 𝐯𝐚𝐫𝐢𝐚𝐧𝐜𝐞 in a principled way:  • Calibration - give more weight to subpopulations where the LLM predictions align with observed outcomes.    • Resampling-based aggregation - average across many random demo sets to neutralize variability.  • Three-way sample splitting - separate data used for examples, prediction, and estimation to preserve independence. The framework is conceptually similar to double machine learning (2ML) - blending causal inference and machine learning. So how do we get this kind of unstructured data? Surveys are one way to go. Ping us at Causara if you'd like to chat about marketing experimentation.