106 posts from all your feeds
This comprehensive guide details intermediate to advanced Seaborn usage for Python data visualization. It covers setting themes, creating various plot types (relational, categorical, distribution, regression), using grid layouts, visualizing correlations, and fine-tuning plots with Matplotlib hooks for clear, accurate communication.
This article presents 10 concise Polars one-liners to accelerate data manipulation workflows. It covers key operations like lazy loading, filtering, and aggregation, positioning Polars as a high-performance alternative to Pandas.
This article showcases 7 agentic AI Chrome extensions, contrasting them with reactive AI. It details tools like Magical, Merlin, Zapier Agents, Recall, BrowserAgent, Taskade AI, and Perplexity AI for automating workflows, enhancing research, and boosting browser productivity.
Learn to build an automated Gmail inbox management agent using n8n. This agent scores incoming emails by sender, content, and category, then routes them to priority-specific actions like labeling, creating tasks, or sending Slack alerts.
365 Data Science offers free, unlimited access to its entire AI and data science learning platform from Nov 6-21, 2025. This initiative provides courses, hands-on projects, and industry-recognized certifications for aspiring tech professionals.
This article explores 5 practical examples of ChatGPT Agents moving beyond conversation to real-world action. It covers automating data cleaning, managing AI customer support, streamlining content production, building research assistants, and orchestrating DevOps. These agents integrate with APIs to automate complex workflows, enhancing efficiency across various sectors.
Learn data science by doing! This post highlights 5 video tutorials for beginners, covering the complete workflow: data cleaning, exploratory analysis, visualization with Plotly, feature engineering, and deploying a model with Streamlit.
This article creatively explains seven essential machine learning algorithms by comparing their strengths and weaknesses to iconic X-Men characters, from Wolverine as a Decision Tree to Jean Grey as a Neural Network.
This article details building a data cleaning pipeline for a messy DoorDash dataset. It covers data exploration, fixing datetime types, imputing missing `store_primary_category` values via a smart `mode()`-based strategy, and dropping remaining NaNs for analysis.
This article highlights five essential, free resources for AI engineers. The recommendations span foundational texts on neural networks, a comprehensive deep learning book, a practical course, a book on agent-based AI, and a paper on ethics.
Master data storytelling with a 7-step guide. This workflow transforms complex analysis into clear, actionable business insights by defining questions, knowing your audience, choosing metrics, and crafting a compelling narrative.
Learn to automate data analytics tasks by encapsulating complex SQL queries into reusable stored procedures. This guide shows how to create a procedure in MySQL and call it from a Python script for streamlined workflows.
Language model hallucinations are not a mysterious flaw but a direct result of training and evaluation methods that reward confident guessing over admitting uncertainty. The issue is rooted in simple classification errors.
Google Stitch is a new AI tool that generates production-ready UI prototypes from simple text or image prompts. This guide shows how to quickly create, iterate, and export designs, accelerating app development.
This article provides a five-step strategic roadmap for businesses to successfully integrate AI. It emphasizes starting with a clear purpose, building a strong data foundation, training staff, scaling smartly, and embedding ethics.
Enhance coding productivity with 5 AI-assisted techniques: context-rich prompting, dual-AI code & review, automated testing, legacy refactoring, and parallel task execution. AI handles repetitive work, letting developers focus on architecture and creativity.
This article guides beginners through building a machine learning regression model in Python using Pandas and Scikit-learn. It covers loading and cleaning a dataset, handling missing values, creating preprocessing pipelines, training a Random Forest model to predict employee income, evaluating its performance, and saving the model for future deployment.
A 2025 report reveals a massive surge in US AI degree programs, with a 167% increase in Master's degrees since 2022. The South, led by Texas, now leads the nation, signaling a decentralization of AI education beyond tech hubs.
This sponsored post highlights SerpApi, a tool that automates data collection from search engines. It solves web scraping challenges by providing a simple API that returns structured JSON data for use in AI models and analytics.
What you need is a system to support the formation of goals within a structure that enables turning these broad ambitions into concrete, achievable targets. This article will provide a simple three-step framework to do so.
Learn to efficiently process large datasets and build ML models using Dask and scikit-learn. This guide covers loading, cleaning, and transforming data with Dask's lazy computation, seamlessly integrating with scikit-learn for scalable workflows.
An introduction to Python metaclasses for data scientists. This post explains how metaclasses act as blueprints for classes, controlling their creation process, much like classes are blueprints for objects.
This post highlights the top 5 open-source video generation models—Wan 2.2, HunyuanVideo, Mochi 1, LTX-Video, and CogVideoX-5B—as powerful, privacy-focused alternatives to closed systems like Google's Veo and OpenAI's Sora.
This guide promotes an efficient, or "lazy," approach to Exploratory Data Analysis (EDA) using Python automation tools like ydata-profiling and Sweetviz to handle repetitive tasks, saving time for deeper, domain-specific analysis.
ChatLLM by Abacus.AI is an all-in-one platform offering access to major AI models like GPT, Claude, and Gemini for a low monthly fee. It provides document analysis, coding, and image generation but has some notable drawbacks.
Python's Global Interpreter Lock (GIL) is being made optional via PEP 703, enabling true multithreading. This promises major performance boosts for CPU-bound tasks but requires developers to manage new concurrency complexities.
This article explores ChatGPT's Study Mode, a feature designed for personalized learning. It weighs the benefits like interactive quizzing against drawbacks such as potential inaccuracies and over-reliance to determine its true value.
This post highlights 10 essential command-line tools for data scientists, updated for 2025. It covers utilities for data retrieval, text processing, parallel execution, and version control to enhance productivity and efficiency.
Discover 10 Python one-liners to optimize Hugging Face Transformers pipelines. Enhance performance and efficiency with GPU acceleration, batching, half-precision, faster tokenization, truncation, and ensure reproducibility for robust NLP/LLM workflows.
This guide champions an efficient, 'lazy' approach to time series forecasting. It shows how to use modern Python libraries like Prophet, AutoARIMA, and AutoML platforms to get accurate predictions quickly, avoiding tedious manual tuning.
This article reviews the top 5 open-source Text-to-Speech (TTS) models: VibeVoice, Orpheus, Kokoro, OpenAudio S1, and XTTS-v2. It highlights their unique features, from multi-speaker podcasts to zero-shot voice cloning.
A step-by-step tutorial on building a complete machine learning web application with Django. Learn to train a scikit-learn model, create a user-friendly web interface, and expose a JSON API for programmatic predictions.
This article provides seven actionable tricks for professionals, particularly in data science, to optimize their LinkedIn profiles. It emphasizes using targeted keywords, showcasing projects, and strategic engagement to attract recruiters.
Google Jules is an asynchronous, agentic AI coding system that automates development tasks like bug fixes, updates, and testing. It integrates with GitHub, executes in secure cloud VMs, and provides transparent plans and diffs for review.
This guide demonstrates building a Text-to-SQL application using OpenAI, FastAPI, and SQLite. It covers setting up the project, connecting to a database, using OpenAI's API for query generation, and containerizing the app with Docker.
This guide provides 10 essential agentic AI interview questions for AI engineers, covering core concepts, tool integration, planning, multi-agent systems, and safety. It emphasizes practical experience, design choices, and understanding trade-offs.
This article offers five practical tips for data scientists to leverage Google's NotebookLM. It covers clustering research, integrating external AI for fact-checking, generating outlines, maintaining dynamic documentation, and refining sources.
Stop wasting time on repetitive tasks. Learn how smart business automation can boost your productivity, cut costs, and get you back hours a week.
A hands-on guide to collecting real-time data using APIs in Python. Learn to use the `requests` and `pandas` libraries with practical examples, from a simple user generator to the complex Eurostat statistical data API.
This article details how data scientists can use Google's NotebookLM to create an "everything" notebook. This centralized knowledge base boosts productivity with semantic search, cross-document synthesis, and advanced querying.
This guide demonstrates how to create synthetic data using random, rule-based, simulation, and AI methods. It provides a step-by-step walkthrough for building a complete portfolio project, from model training to a Streamlit app.
This article reviews the top 5 agentic coding CLI tools: Claude Code, OpenCode, Droid, Codex CLI, and Gemini CLI. It shares personal experiences, pros, cons, and installation commands, highlighting tools for daily tasks, customization, debugging, and leveraging LLMs. Node.js is a common prerequisite.
This article presents a curated list of 10 free APIs essential for data science projects. It categorizes them for easy access to foundational datasets, web scraping, geospatial information, financial markets, and social media data.
This guide details a systematic approach to building robust data pipelines, emphasizing software engineering principles. It covers validation, idempotency, schema evolution, backpressure, data quality monitoring, observability, and testing to prevent failures and reduce maintenance.
An introductory guide for Python developers learning TypeScript. It compares key concepts like type systems, classes, and functions, highlighting how TypeScript provides robust, compile-time safety that prevents common Python runtime errors.
Discover 10 top free newsletters for busy data scientists to efficiently stay updated on machine learning, AI, statistics, and data engineering. Curated content helps cut through noise, offering practical insights and career advice.
This post introduces the Model Context Protocol (MCP), a standard for AI systems to securely interact with external tools and data. It defines three roles: hosts (apps), clients (AI), and servers (resource wrappers) for reusable integration.
This article curates a list of 5 free, essential books for LLM engineers. The recommendations cover diverse, crucial topics including foundational theory, NLP, system scaling on TPUs, model interpretability, and cybersecurity risks.
This article offers 7 practical, copy-paste prompt engineering templates for LLMs across diverse tasks, including job applications, coding, creative writing, and business strategy, emphasizing structured and guided inputs for superior outputs.
A product data scientist shares their interview preparation plan for a large tech company, emphasizing skills that differentiate a product role from traditional data science: advanced SQL, applied statistics, and business acumen.
An introduction to Firebase Studio, Google's cloud-based IDE. It integrates Firebase services and Gemini AI to streamline full-stack app development, enabling rapid prototyping and deployment with zero local setup.
A 7-step guide for data analysts to transition their skills from Excel to Python. It covers mapping existing knowledge, learning fundamentals with libraries like Pandas, practicing on real data, and integrating Python with Excel.
Transitioning from reactive hustle to proactive structure by building simple, repeatable processes. If you are looking for practical ways to get started in the shift from hustle to structure, this article has you covered.
A step-by-step guide to containerizing a Python Flask app. Learn to write a Dockerfile, build a Docker image, test it locally, and publish it to Docker Hub, making your application portable and easily shareable.
This article introduces Z.AI's GLM-4.6 coding model and its affordable ~$3/month subscription, the GLM Coding Plan. It provides a step-by-step tutorial on integrating it with OpenCode to build a complete website from a single prompt.
Don't default to SQL for every data task. This post details when spreadsheets are the better choice: for small data, quick tasks, collaboration, visualization, and when working with non-technical teams.
This post explores 10 psychological reasons why audiences misinterpret data, from cognitive biases to poor visualization. It provides actionable fixes for data storytellers to present information clearly and drive correct decisions.
If you've been hearing about "the cloud" for years but still aren't sure what it means for your business, we get it. Let's cut through the noise.
Qwen Code is a new agentic CLI programming tool powered by the Qwen3-Coder model. It enhances developer productivity by understanding codebases, suggesting optimizations, and automating tasks directly from the command line.
This article explores the best local coding LLMs for developers, highlighting options like GLM-4, DeepSeekCoder V2, Qwen3-Coder, Codestral, and Code Llama. It details their features, performance, context windows, and hardware requirements.
This article explores 7 popular Python package managers: uv, pip, Poetry, Conda, Miniconda, Mamba, and Pixi. It details their features, installation, and ideal use cases, from beginner-friendly Anaconda to fast alternatives like uv and Mamba for data science.
Enhance customer engagement with Microsoft Dynamics 365. This AI-powered platform unifies data across sales, marketing, and service, turning fragmented customer insights into actionable, personalized experiences.
This guide explains cross-validation, a technique for robustly evaluating machine learning models. It details why it's superior to a single train/test split and covers key methods like k-fold, stratified, and time-series CV.
Discover five fun, beginner-friendly AI agent projects, from a pure Python calendar assistant to advanced research bots. This guide provides curated video tutorials to help you build agents that can act, reason, and automate tasks.
A data scientist shares that most roles require applied statistics for business problems, not deep academic theory. Focus on concepts like A/B testing, ML model interpretation, and descriptive analysis. Learn core concepts for interviews and advanced skills on the job.
A concise guide for data analysts on 15 essential SQL queries. It covers fundamental commands like SELECT, WHERE, and GROUP BY, to advanced functions like JOINs, CASE, and window functions for effective data extraction and transformation.
This guide explores advanced Pandas GroupBy techniques beyond simple sums and means. It covers the distinct uses of agg, transform, apply, and filter for complex scenarios like conditional logic, weighted metrics, and time-series analysis.
This post highlights seven free remote Model Context Protocol (MCP) servers for developers. These tools, including integrations for GitHub, Figma, and Notion, connect AI assistants to essential services to streamline workflows.
AIjacking, a new threat exploiting LLMs via prompt injection, allows AI agents to perform unauthorized actions like data exfiltration without human interaction, bypassing traditional security. The article outlines practical defenses and emphasizes a security-first AI approach.
A performance benchmark of DuckDB, SQLite, and Pandas on a 1M row dataset. The test compared speed and memory usage for common data analysis tasks, revealing DuckDB's consistent high performance and balanced efficiency.
This article highlights 7 essential Python libraries for analytics engineers, covering data manipulation (Polars), quality (Great Expectations), transformation (dbt), orchestration (Prefect), and more to streamline workflows.
By 2026, NLP will evolve beyond current models, focusing on five key trends: efficient attention mechanisms, autonomous language agents, world models for reasoning, knowledge graphs for context, and on-device NLP for speed and privacy.
A comprehensive guide to Google AI Studio, covering account setup, interface features, and model selection. Learn to prototype with Gemini, generate code and images, and build AI applications directly from the web-based workspace.
This article outlines five practical Python scripts designed to automate common, time-consuming tasks for data analysts, such as report formatting, data reconciliation, dashboard creation, and scheduled data pulls.
This post highlights five essential Docker containers to build a robust AI infrastructure: JupyterLab for experimentation, Airflow for orchestration, MLflow for tracking, Redis for caching, and FastAPI for serving models.
Discover 10 powerful Python one-liners for common CSV tasks. This guide shows how to sum, group, filter, and analyze CSV data efficiently using built-in modules, perfect for quick data exploration without external libraries like pandas.
An introduction to vLLM, an open-source serving engine that optimizes LLM inference. It uses a core innovation called PagedAttention to achieve high throughput, low latency, and efficient memory use for production applications.
This post explores Generative AI's impact on the Software Development Lifecycle (SDLC), weighing its productivity benefits against limitations like the need for human oversight, security risks, and struggles with complex, novel tasks.
A practical crash course on using Weights & Biases (W&B) for MLOps. Learn to track experiments, version datasets and models with Artifacts, run hyperparameter sweeps, and improve reproducibility in your ML workflows.
This tutorial guides users through prototyping a lightweight RAG system using Airtable for knowledge, OpenAI's GPT models for generation, and Pipedream for no-code orchestration. It details setting up the workflow and offers code and AI-agent methods for building it.
This guide demonstrates how to use Google's NotebookLM to enhance technical interview preparation. It uses a Meta recommendation system problem to showcase how the AI tool creates summaries, quizzes, and visual aids to deepen understanding.
This guide explains why tracking token usage in LLM apps is vital for cost and performance. It provides a step-by-step tutorial on using LangSmith with LangChain and Hugging Face to monitor and visualize token consumption.
How do you know if you're ready to take the AI plunge? Here are five dead giveaways that AI could transform how you work.
This article explores seven powerful and free alternatives to ChatGPT for tasks like research, coding, and content creation. It highlights the unique features of Microsoft Copilot, Google Gemini, Grok, You.com, and others.
Beyond technical tests, data science interviews have a 'hidden curriculum.' This post reveals the non-technical skills companies truly evaluate, such as business translation, handling ambiguity, and understanding trade-offs.
This guide introduces VibeVoice, Microsoft's open-source, next-gen Text-to-Speech framework for expressive, multi-speaker audio. Learn to set up VibeVoice-1.5B on Google Colab, download the model, create transcripts, run inference, and troubleshoot common issues. It highlights VibeVoice's quality and open-source benefits.
A practical guide to API development for web apps and data products. Covers essential principles from user-centric design and RESTful practices to robust security, scaling strategies, and the importance of clear documentation.
This post outlines a free, 7-day mini-course for beginners learning Python for data science. It covers fundamental skills like data structures, file I/O, string manipulation, and error handling using only core Python.
Big Tech's massive investments accelerate AI development, but their dominance over cloud infrastructure and the AI supply chain raises serious concerns about stifling competition, market control, and significant ethical challenges.
Explore 10 practical Python one-liners for data engineering using pandas. This guide covers common tasks like parsing JSON, analyzing performance logs, detecting schema changes, monitoring APIs, and optimizing memory usage.
This tutorial introduces Reflex, an open-source library for building scalable, full-stack web applications entirely in Python. It covers installation, core concepts like state management, and guides you through building a to-do list app.
This guide explores Google's "Nano Banana" (Gemini 2.5 Flash) AI image model. Learn its advanced features, like multi-image composition and semantic inpainting, with practical prompting strategies and usage tips.
Learn to build a custom interactive command-line shell in Python using the built-in `cmd` module. This step-by-step guide covers creating commands, parsing arguments with `shlex`, adding a help system, and creating aliases.
A data scientist shares 10 essential bookmarks for staying updated and productive. The list covers everything from new research and trending code to unique datasets, quick visualization tools, and job listings.
Learn the fundamentals of data analysis with Polars, a high-performance Python library. This beginner-friendly guide uses a coffee shop dataset to walk you through installation, data creation, manipulation, and analysis.
This article compares three feature selection techniques—Filter, Wrapper (RFE), and Embedded (Lasso)—using the scikit-learn Diabetes dataset. The experiment concludes that the Embedded method, Lasso, offered the best performance.
This article introduces Data Commons, Google's open-source initiative to organize public data via a knowledge graph. It details using the new Python API client to access datasets, covering API key setup, library installation, and fetching statistical variables and entities using DCIDs, with examples for Pandas DataFrames.
This post highlights 5 signs your business is a prime cyberattack target: weak passwords, outdated software, untrained staff, poor backups, and no monitoring. It stresses that SMBs are heavily targeted and prevention is key.
This article provides five practical tips to build more efficient and useful Streamlit dashboards. It covers performance enhancement with caching, improving UX with input batching, state management, displaying KPIs, and extending features.
A data scientist explains their decision to leave a six-figure freelance career for a lower-paying, full-time job. The choice prioritizes long-term career security, paid learning, and building skills less replaceable by AI.
This guide provides a five-step framework for successful AI integration: defining the problem, building a strong data foundation, upskilling employees, starting with small pilots, and embedding ethical AI practices from the start.
Discover Data Observability, the process of monitoring data system health. This post breaks down its five pillars (freshness, volume, schema, distribution, lineage), its benefits, lifecycle, and key industry tools to ensure reliable analytics.
Bay Path University's online Master's in Applied Data Science helps working professionals transform data into real impact. The program offers practical, hands-on learning, including Generative AI expertise, to advance careers.
A beginner's guide to LangExtract, Google's open-source Python library. It leverages LLMs like Gemini and OpenAI to extract structured information from unstructured text using simple prompts and few-shot examples.