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# Ryan & Matt Data Science

Website based around Data Science

## Sitemaps

- [XML Sitemap](https://ryanandmattdatascience.com/sitemap.xml): Contains all public & indexable URLs for this website.

## Posts

- [Blog](https://ryanandmattdatascience.com/blog/)
- [AI Email Triage & Response System](https://ryanandmattdatascience.com/ai-email-triage-automation-case-study/) - How we built an n8n workflow that classifies and drafts replies to every incoming email — scoring 29/30 in the n8n Inbox Inferno challenge. See the full build and business ROI.
- [97% Accurate Product Categorization at Scale](https://ryanandmattdatascience.com/97-percent-product-categorization-5585-categories/) - 97% Accurate Product Categorization at ScaleHow we built an ML pipeline that classifies marketplace products into 5,585 categories with 97% accuracy — replacing a manual process that could not keep pace with inventory volume. The ChallengeA large online marketplace had a categorization problem that only got worse as they scaled. With 5,585 product categories and
- [Customer Support Insights Extraction](https://ryanandmattdatascience.com/customer-support-insights-extraction/) - Customer Support Insights ExtractionHow we built an AI pipeline that reads through customer support call transcripts at scale and turns raw conversation data into structured business intelligence — surfacing the issues your team is too busy to notice. The ChallengeCustomer support calls are one of the most valuable and most ignored data sources in a
- [AI Sales Call Quality Scoring](https://ryanandmattdatascience.com/ai-sales-call-quality-scoring/) - AI Sales Call Quality ScoringHow we built an AI system that reads every sales call transcript, scores each section against a weighted rubric, and flags coaching opportunities — giving managers instant QC on every rep without listening to a single call. The ChallengeMost sales teams have a quality problem they can’t actually see. Managers know
- [Fitness Creator Lead Qualification Bot](https://ryanandmattdatascience.com/fitness-creator-lead-qualification-automation/) - Fitness Creator Lead Qualification BotHow we automated prospect research for a fitness creator platform — scraping follower data, analysing post content, and pushing only qualified leads to the sales team in Slack. The ChallengeA SaaS platform built for fitness creators had a lead quality problem. Their signup form was pulling in a broad mix of
- [Cancer Clinical Trial Eligibility Screening](https://ryanandmattdatascience.com/cancer-clinical-trial-eligibility-ai/) - Cancer Clinical Trial Eligibility ScreeningHow we built an AI agent that reads unstructured doctor notes, extracts clinical data, and determines patient eligibility for cancer trials — cutting hours of manual chart review down to minutes. The ChallengeClinical trial coordinators face a documentation bottleneck. Patient records are written in free-form narrative by clinicians — dense, unstructured,
- [Claude Code for Data Analysts: 10 Workflows That Save Hours Every Week](https://ryanandmattdatascience.com/claude-code-for-data-analysts/) - Claude Code for Data Analysts: 10 Workflows That Save Hours Every Week (2026) The data analyst role is changing fast. The analysts pulling ahead right now are not spending more time learning SQL or Python. They are using Claude Code for data analysts to do in 5 minutes what used to take 5 hours. This
- [Claude Code for Data Science: 10 Real Examples (2026)](https://ryanandmattdatascience.com/claude-code-for-data-science/) - Learn how to use Claude Code for data science with 10 real examples: EDA, data cleaning, SQL, APIs & more. Save hours every week.
- [AI Sales Call Analysis: How to Score Every Rep Call Automatically in 2026](https://ryanandmattdatascience.com/ai-sales-call-analysis/) - Learn how to set up AI sales call analysis to score every rep call automatically. No more manual QA. See the full workflow.
- [CRM AI Agent: How to Automate Your Sales Pipeline in 2026](https://ryanandmattdatascience.com/crm-ai-agent/) - Learn how a CRM AI agent connects HubSpot, Gmail & Slack to automate follow-ups and surface deal insights daily.
- [How to Set Up Claude Cowork Connectors Step-by-Step (2026)](https://ryanandmattdatascience.com/claude-cowork-connectors/) - Learn how to set up Claude Cowork connectors for Gmail, Calendar, and more. Control permissions and connect 100s of apps.
- [n8n streamlit](https://ryanandmattdatascience.com/n8n-streamlit/) - Learn how to combine n8n and Streamlit — trigger workflows from Streamlit, feed data to dashboards, build control panels, and create powerful data apps with automation backends.
- [n8n PDF Generator: Create and Send PDFs Automatically](https://ryanandmattdatascience.com/n8n-pdf-generator-2/) - Learn how to generate PDFs in n8n — HTML templates, PDF APIs, email attachments, Google Drive storage, and a complete end-to-end automated invoice pipeline.
- [n8n Date & Time Node](https://ryanandmattdatascience.com/n8n-date-time-node/) - Master the n8n Date & Time node — format dates, calculate intervals, convert time zones, extract date parts, and build calendar-aware workflows with practical examples.
- [n8n Basic LLM Chain Node: Your First Step Into AI Workflows](https://ryanandmattdatascience.com/n8n-basic-llm-chain/) - Learn how the n8n Basic LLM Chain node works — prompt design, dynamic expressions, chaining multiple LLM calls, and when to use it versus the AI Agent node.
- [n8n Error Handling: Build Resilient Workflows That Never Silently Fail](https://ryanandmattdatascience.com/n8n-error-handling/) - Master n8n error handling — error workflows, Stop and Error node, retry logic, Continue on Fail, and monitoring best practices to keep your automations production-ready.
- [n8n Evaluations: How to Test and Measure Your AI Workflows](https://ryanandmattdatascience.com/n8n-evaluations/) - Learn how to use n8n evaluations to test AI workflows — build test datasets, define scoring criteria, run LLM-as-judge evaluations, and continuously improve output quality.
- [n8n Call Workflow Tool: Give Your AI Agent Superpowers](https://ryanandmattdatascience.com/n8n-call-workflow-tool/) - Learn how the n8n Call Workflow tool works — attach any n8n workflow to an AI agent as a callable tool, write effective descriptions, pass arguments, and build capable agents.
- [n8n execute sub-workflow trigger node](https://ryanandmattdatascience.com/n8n-execute-sub-workflow-trigger-node/) - Learn how to use n8n sub-workflows with the Execute Workflow and Execute Workflow Trigger nodes — modular design, data passing, execution modes, and real examples.
- [n8n Summarization Chain: Summarize Any Document with AI](https://ryanandmattdatascience.com/n8n-summarization-chain/) - Learn how the n8n Summarization Chain node works — map reduce vs refine modes, custom prompts, document loaders, and practical AI summarization workflow examples.
- [n8n Sticky Notes: How to Organize and Document Your Workflows](https://ryanandmattdatascience.com/n8n-sticky-notes/) - Learn how to use n8n sticky notes to document complex workflows — add context, explain logic, mark sections, and make your automations easier to maintain and share.
- [n8n Limit Node: Control How Many Items Flow Through Your Workflow](https://ryanandmattdatascience.com/n8n-limit-node/) - Learn how the n8n Limit node works — cap item counts with Keep From Beginning or End modes, combine with Sort and Filter, and simplify workflow logic without custom code.
- [n8n Compare Datasets Node: Find Differences Between Two Data Sources](https://ryanandmattdatascience.com/n8n-compare-datasets-node/) - Learn how the n8n Compare Datasets node works — detect new, deleted, and changed records between two data sources with four output branches and practical sync examples.
- [n8n Merge Node: Complete Guide to Combining Workflow Data](https://ryanandmattdatascience.com/n8n-merge-node/) - Master the n8n Merge node — Append, Combine (Inner/Outer/Left/Right join), and Multiplex modes explained with real workflow examples and use cases.
- [n8n Data Tables: Native Table Storage in n8n Explained](https://ryanandmattdatascience.com/n8n-data-tables/) - Learn how n8n data tables work — create tables, insert and query rows, use upsert, and build persistent storage into your workflows without an external database.
- [n8n sort node](https://ryanandmattdatascience.com/n8n-sort-node/) - Learn how to use the n8n Sort node to reorder workflow data. Covers simple and advanced modes, multi-field sorting, sorting strings and dates, with practical examples.
- [n8n Trigger node](https://ryanandmattdatascience.com/n8n-trigger-node/) - Master every n8n trigger node — Schedule, Webhook, Manual, Chat, Form, and app-specific triggers. Learn when to use each one with practical workflow examples.
- [Connect Google Drive to N8N](https://ryanandmattdatascience.com/connect-google-drive-to-n8n/) - Learn how to connect Google Drive to n8n step by step. Set up OAuth credentials, upload and download files, list folders, and build powerful Drive automation workflows.
- [n8n Edit Fields Node (Set Node): 15+ Examples for Data Transformation](https://ryanandmattdatascience.com/n8n-edit-fields-set-node/) - Master the n8n Edit Fields (Set) node with 15+ real examples. Learn manual mapping, expressions, renaming fields, type conversion, and data transformation workflows.
- [n8n Switch Node: 8 Examples for Rules Mode and Expression Routing](https://ryanandmattdatascience.com/n8n-switch-node/) - Master the n8n switch node with 8 practical examples. Learn rules mode vs expression mode, string and number comparisons, output renaming, and the all-matching-outputs option for fan-out workflows.
- [n8n sentiment analysis node](https://ryanandmattdatascience.com/n8n-sentiment-analysis-node/) - Learn how to use the n8n sentiment analysis node to classify text as positive, negative, or neutral. Covers settings, custom categories, detailed results, system prompts, and Hugging Face domain-specific models.
- [n8n Chat Hub: Complete Guide to Building Chat Interfaces](https://ryanandmattdatascience.com/n8n-chat-hub/) - Learn how to use n8n Chat Hub to build ChatGPT-style interfaces on top of your workflows. Covers model switching, chat-only user permissions, memory setup, complex workflow connections, and real-world use cases.
- [n8n Nodes List: Every Core AI Node You Should Know](https://ryanandmattdatascience.com/n8n-nodes/) - Explore the complete n8n nodes list for AI automation — from text classifier and information extractor to guardrails, Q&A chain, and model-specific integrations. Learn which core n8n nodes to use instead of the AI agent.
- [How to Learn n8n: A Complete Roadmap from Beginner to Advanced AI Workflows](https://ryanandmattdatascience.com/how-to-learn-n8n/) - Learn n8n with this structured roadmap covering core nodes, APIs, AI fundamentals, RAG, and multimodal workflows. Go from beginner to building complex AI automations in 1-3 months.
- [n8n Beginner Guide: Essential Nodes, Concepts, and Workflow Patterns](https://ryanandmattdatascience.com/n8n-beginner-guide/) - Learn n8n from scratch with this comprehensive beginner guide. Covers triggers, nodes, data flow, Split Out, Merge, If/Switch, binary files, error handling, and more.
- [n8n REST API Tutorial: GET, POST, PUT, and DELETE with the HTTP Request Node](https://ryanandmattdatascience.com/n8n-rest-api/) - Learn how to use the n8n HTTP Request node to call REST APIs. Covers GET, POST, PUT, PATCH, and DELETE with real examples using JSONPlaceholder — no authentication required.
- [n8n HTTP Request Node Pagination: Complete Guide with Real API Examples](https://ryanandmattdatascience.com/n8n-http-request-pagination/) - Learn how to paginate API requests in n8n using the HTTP Request node. Covers response-based pagination, rate limits, limit/offset, and cleaning data with Edit Fields and Split Out.
- [How to Use OpenRouter with n8n: Model Selector, Fallback Models, and More](https://ryanandmattdatascience.com/n8n-openrouter/) - Learn how to use OpenRouter with n8n to access hundreds of AI models with one API key. Set up model selectors, fallback models, and build more resilient AI workflows.
- [n8n google search leads](https://ryanandmattdatascience.com/n8n-google-search-results-scraper/) - Learn how to scrape Google search results with n8n and Apify. Automate SERP data collection, process results with Python and AI, and send leads to Google Sheets.
- [N8N Scrape Instagram](https://ryanandmattdatascience.com/n8n-scrape-instagram/) - Build an n8n workflow that scrapes Instagram hashtag posts, extracts creator usernames, profiles each account with Apify, filters by follower count, and exports leads to Google Sheets.
- [n8n scrape tiktok](https://ryanandmattdatascience.com/n8n-scrape-tiktok/) - Learn how to scrape TikTok account stats and post data with n8n and the Apify TikTok actor. Filter high-performing posts and write results to Google Sheets automatically.
- [How to Scrape Reddit with n8n: RSS Feeds and Apify Workflow Guide](https://ryanandmattdatascience.com/n8n-reddit-scraper/) - Learn two ways to scrape Reddit in n8n without an API key: free RSS feeds via FetchRSS and the Apify Reddit Scraper actor with AI text classification and Google Sheets output.
- [N8N Apify](https://ryanandmattdatascience.com/n8n-apify/) - Learn how to connect Apify to n8n and build an automated lead generation workflow. Scrape Google Maps, enrich leads with AI, and output to Google Sheets — no code required.
- [n8n Webhook](https://ryanandmattdatascience.com/n8n-webhook/) - Master the n8n Webhook node with this complete guide: test vs production URLs, query parameters, Postman and cURL testing, Basic and Header authentication, respond to webhook, logging, and the subworkflow pattern.
- [n8n Split Out node](https://ryanandmattdatascience.com/n8n-split-out-node/) - Learn how to use the n8n Split Out node with 4 practical examples: splitting JSON data, Google Sheets, arrays, and combining sheets with arrays for powerful one-to-many workflows.
- [N8N Hubspot](https://ryanandmattdatascience.com/n8n-hubspot/) - Learn how to update HubSpot contacts in n8n using two approaches: the built-in HubSpot node for simple updates and the HubSpot Batch API for bulk updates of 100 contacts every 10 seconds.
- [n8n aggregate node](https://ryanandmattdatascience.com/n8n-aggregate-node/) - Learn how to use the n8n Aggregate node to combine multiple items into one. Covers Individual Fields mode, All Item Data mode, and Code node math with real examples.
- [Claude Cowork Email Automation: Step-by-Step Guide (2026)](https://ryanandmattdatascience.com/claude-cowork-email-automation/) - Learn how to connect your email to Claude Cowork, draft replies, and set up daily inbox automations — no code required.
- [How to Use Claude Cowork Scheduled Tasks (2026)](https://ryanandmattdatascience.com/claude-cowork-scheduled-tasks/) - Learn how to set up Claude Cowork scheduled tasks to automate daily briefings, reports, and more. Complete step-by-step guide.
- [n8n http request node](https://ryanandmattdatascience.com/n8n-http-request-node/) - Learn how to use the n8n HTTP Request node to connect to any API. Covers methods, authentication, cURL import, SerpAPI and Appify examples, and common errors.
- [n8n information extractor node](https://ryanandmattdatascience.com/n8n-information-extractor-node/) - Learn how to use the n8n Information Extractor node to pull structured data from any text. All 3 schema types and 4 real examples covered.
- [n8n text classifier node](https://ryanandmattdatascience.com/n8n-text-classifier-node/) - Learn how to use the n8n Text Classifier node to route customer support, filter emails, and categorize text automatically.
- [n8n if Node](https://ryanandmattdatascience.com/n8n-if-node/) - Master the n8n IF node with 11 real examples. Covers all data types (string, number, boolean, date, array, object), AND/OR logic, Ignore Case, type conversion, nested IF nodes, and when to use Switch or Code node instead.
- [n8n remove duplicates node](https://ryanandmattdatascience.com/n8n-remove-duplicates-node/) - Learn how to use the n8n Remove Duplicates node to eliminate duplicate items in your workflows. Covers all 3 operations: Remove Items Seen in Previous Executions, Remove Items Repeated Within Current Run, and Remove Duplicate Input Items — with real examples.
- [n8n convert to file node](https://ryanandmattdatascience.com/n8n-convert-to-file-node/) - The n8n Convert to File node turns workflow data into downloadable files. Full guide: CSV, XLSX, JSON, Base64 images, text, and ICS.
- [How to use Monday.com in n8n](https://ryanandmattdatascience.com/how-to-use-monday-com-in-n8n/) - Step-by-step guide to monday.com in n8n: API credentials, boards, items, webhooks, and a real Google Drive automation.
- [n8n Guardrails: Complete Guide to AI Safety in Your Workflows (2026)](https://ryanandmattdatascience.com/n8n-guardrails/) - Learn how to use n8n guardrails to block PII, jailbreaks, API keys & more. 13 examples covered.
- [n8n Email Automation: Build an AI Classifier and Autoresponder (2026)](https://ryanandmattdatascience.com/n8n-email-automation/) - Learn how to build an n8n email classifier and autoresponder using the Text Classifier node, Google Sheets, and an AI agent.
- [n8n loop over items node](https://ryanandmattdatascience.com/n8n-loop-over-items-node/) - Learn when (and when not) to use the n8n Loop Over Items node. Batch processing, rate limits, and the Wait node explained.
- [n8n Binary Data](https://ryanandmattdatascience.com/n8n-binary-data/) - Learn how n8n binary data works: get files in, extract, convert, analyze images, handle Base64, and use the new Oct 2025 expression update.
- [n8n rss feed node](https://ryanandmattdatascience.com/n8n-rss-feed-node/) - Learn how to use the n8n RSS feed node to read feeds, trigger workflows, and build an AI email newsletter. Step-by-step guide with examples.
- [n8n Summarize Node: How to Count, Sum, and Group Data in Any Workflow (2026)](https://ryanandmattdatascience.com/n8n-summarize-node/) - Learn how to use the n8n Summarize node to count, sum, average, and group data in any workflow. Includes 11 real examples with step-by-step setup.
- [How to Use Claude Cowork Ideas (2026)](https://ryanandmattdatascience.com/claude-cowork-ideas/) - Never stare at a blank prompt again. The Claude Cowork Ideas tab suggests tasks based on your tools. Learn how to use it.
- [How to Connect ClickUp to n8n: Step-by-Step (2026)](https://ryanandmattdatascience.com/n8n-clickup-integration/) - Connect ClickUp to n8n: personal API token setup, 57 node actions, 27 triggers, transcript-to-task workflow, and automated client onboarding.
- [How to Connect Notion to n8n: Step-by-Step (2026)](https://ryanandmattdatascience.com/n8n-notion-integration/) - Connect Notion to n8n in 5 steps. Create pages, search databases, and automate tasks. Free JSON included.
- [How to Set Up the n8n Slack Integration (2026)](https://ryanandmattdatascience.com/n8n-slack-integration/) - Step-by-step n8n Slack integration tutorial. Set up credentials, triggers, send messages, and build approval flows.
- [How to Connect Airtable to n8n: Complete Guide (2026)](https://ryanandmattdatascience.com/n8n-airtable-integration/) - Connect Airtable to n8n: access token setup, 8 native actions, HTTP request for advanced ops, webhook trigger.
- [Claude Cowork Tutorial: Complete Beginner's Course (2026)](https://ryanandmattdatascience.com/claude-cowork-tutorial/) - Learn Claude Cowork from scratch. Files, spreadsheets, browser automation, skills, and MCP connectors covered in one guide.
- [What Are Claude Cowork Projects? Setup, Memory & Scheduled Tasks (2026)](https://ryanandmattdatascience.com/claude-cowork-projects/) - Learn how to set up Claude Cowork Projects with persistent memory, custom instructions, and scheduled tasks. Step-by-step setup guide.
- [Claude Cowork Excel: 10 Ways to Automate Spreadsheets in 2026](https://ryanandmattdatascience.com/claude-cowork-excel/) - Use Claude Cowork to clean data, write formulas, and build charts in plain English. 10 real examples inside.
- [Claude Cowork Skills: How to Use, Create, and Update Them (2026)](https://ryanandmattdatascience.com/claude-cowork-skills/) - Learn how to use, create, and update Claude Cowork skills. Step-by-step guide with real examples from the Skill Creator.
- [Claude Cowork Dispatch: How to Control Claude from Your Phone (2026)](https://ryanandmattdatascience.com/claude-cowork-dispatch/) - Set up Claude Cowork Dispatch to send tasks from your phone and let Claude work on your desktop. Step-by-step guide.
- [Claude Cowork Web Scraping: How to Extract Data Without Code (2026)](https://ryanandmattdatascience.com/claude-cowork-web-scraping/) - Use Claude Cowork to scrape websites without code. Real eBay demo, safety rules, and when Python beats Cowork.
- [Claude Cowork Plugins: How to Install, Customize, and Update (2026)](https://ryanandmattdatascience.com/claude-cowork-plugins/) - Install, customize, and update Claude Cowork plugins in minutes. See all plugin categories and what each one does.
- [25 Claude Cowork Tips and Tricks to Get More Done (2026)](https://ryanandmattdatascience.com/claude-cowork-tips/) - 25 Claude Cowork tips and tricks covering memory, sub-agents, plugins, and more. Start using Cowork at full power.
- [Claude Cowork PowerPoint: Turn Any Document Into a Slide Deck (2026)](https://ryanandmattdatascience.com/claude-cowork-powerpoint/) - Turn any doc into a polished PowerPoint with Claude Cowork. Full step-by-step + claude.md tips.
- [n8n telegram](https://ryanandmattdatascience.com/n8n-telegram/) - Learn how to use Telegram in n8n workflows: bot creation, triggers, sending messages, and human-in-the-loop approvals
- [n8n Google Sheets](https://ryanandmattdatascience.com/n8n-google-sheets/) - Learn how to connect n8n to Google Sheets for automated workflows—ideal for data ops, reporting, and integration
- [n8n Perplexity](https://ryanandmattdatascience.com/n8n-perplexity/) - Learn how to quickly set up and use Perplexity in n8n
- [n8n Form trigger node](https://ryanandmattdatascience.com/n8n-form-trigger-node/) - A complete guide to the n8n Form Trigger node — how to build custom forms, configure field types, handle responses, and connect submissions to any downstream workflow.
- [n8n RAG Text Splitters](https://ryanandmattdatascience.com/n8n-rag-text-splitters/) - Retrieval-Augmented Generation (RAG) is one of the most powerful techniques for building intelligent, context-aware AI applications. One of the earliest and most crucial steps in a RAG pipeline is chunking, i.e splitting large documents into smaller, digestible pieces so they can be efficiently processed, embedded, and stored in a vector database.This article explains how chunking
- [n8n human in the loop](https://ryanandmattdatascience.com/n8n-human-in-the-loop/) - Learn how to add human in the loop to n8n workflows. 5 real examples using Gmail, Telegram, and Chat. Start building
- [Python Cumulative distribution function](https://ryanandmattdatascience.com/python-cumulative-distribution-function/) - Learn how to compute and plot cumulative distribution functions (CDF) in Python using real data.
- [ACF Autocorrelation Function](https://ryanandmattdatascience.com/acf-autocorrelation-function/) - Learn what the autocorrelation function is, how it’s calculated, and why it's essential in time series analysis. Explore key formulas and examples
- [Filter Pandas Dataframe with multiple conditions](https://ryanandmattdatascience.com/pandas-dataframe-filter-with-multiple-conditions/) - Speed up your data filtering in Pandas using multiple conditions with ease. Clear examples and pro tips to write cleaner code.
- [n8n RAG Embeddings with OpenAI](https://ryanandmattdatascience.com/n8n-rag-embeddings-with-openai/) - Retrieval-Augmented Generation (RAG) systems have become one of the most effective architectures for building intelligent, context-aware AI applications. Whether you’re developing a chatbot that answers questions based on documentation or an AI assistant that searches through proprietary files, one of the most critical components in your RAG pipeline is vector embeddings.This article will explain what
- [What is sbert](https://ryanandmattdatascience.com/what-is-sbert/) - Welcome! This article is the first in a new series that will look at Retrieval-Augmented Generation (RAG), which is one of the best ways to combine external information with large language models (LLMs).we’ll dive into the foundation of RAG pipelines: the embedding model. Specifically, we’ll explore SBERT (Sentence-BERT) What Is RAG? A Quick Overview Before
- [n8n wait node](https://ryanandmattdatascience.com/n8n-wait-node/) - The n8n Wait node seems way too easy at first glance. It doesn’t change data, talk to outside services, or make new files; all it does is make your workflow pause. But that Pause can often be the difference between a process that breaks all the time and one that always works well.In this article,
- [How to Install Streamlit](https://ryanandmattdatascience.com/how-to-install-streamlit/) - Streamlit is an open-source Python library that allows you to create interactive, data-driven web applications quickly. It is widely used for data science, machine learning dashboards, and visualization projects.Let us look at the installation of streamlit.Need a Streamlit developer? Click here Prerequisites Before installing Streamlit, ensure you have the following:Python: Streamlit requires Python 3.9 and
- [Streamlit Metric](https://ryanandmattdatascience.com/streamlit-metric/) - When building dashboards and data apps, you often need to display key performance indicators (KPIs) at a glance—such as revenue, number of users, or conversion rates. In Streamlit, the st.metric function makes it simple to show these KPIs with labels, values, and deltas (changes) in a clean, professional way.In this article, we’ll explore how to
- [Apollo N8N](https://ryanandmattdatascience.com/apollo-n8n/) - In this n8n tutorial, you will learn how to set up a workflow to utilize Apollo for cheap lead generation
- [N8N Gmail](https://ryanandmattdatascience.com/n8n-gmail/) - Learn how to automate Gmail in n8n with a simple workflow. In addition this tutorial shows you how to set it up for the first time.
- [n8n google maps scraper](https://ryanandmattdatascience.com/n8n-google-maps-scraper/) - In just a few minutes, learn how to scrape Google Maps for contact information and reviews with the help of n8n and Apify
- [Streamlit header](https://ryanandmattdatascience.com/streamlit-header/) - A header is a formatted text element that serves as a section title.Streamlit headers help divide your app into logical sections. Need a Streamlit developer? Click Here Syntax st.header(body, anchor=None, *, help=None, divider=False, width=”stretch”)ParameterType / ValuesDescriptionbodystrThe text to display as GitHub-flavored Markdown. Supports directives from st.markdown.anchorstr, False, or NoneAnchor name for linking (#anchor in URL). If
- [n8n Model Context Protocol](https://ryanandmattdatascience.com/n8n-model-context-protocol/) - Learn how to use MCP in n8n with both Cloud and Self Hosted
- [n8n LinkedIn Lead Generator](https://ryanandmattdatascience.com/n8n-linkedin-lead-generator/) - Learn how to scrape LinkedIn for leads with the help of n8n and Apify with this workflow
- [Streamlit Title](https://ryanandmattdatascience.com/streamlit-title/) - Streamlit st.title displays text in title formatting.The st.title() function in Streamlit displays a large, bold title at the top of your app.It is typically used for headings, titles, or to highlight important sections of your application. Need a Streamlit Developer? Click here Syntax import streamlit as st st.title(body, anchor=None, *, help=None, width="stretch") Example import streamlit as
- [Streamlit Async](https://ryanandmattdatascience.com/streamlit-async/) - Streamlit runs Python scripts top-to-bottom when ever a user interacts with widget.Streamlit is synchronous by default, meaning each function waits for the previous one to finish.The problem arises when we are fetching remote APIs, databases or long computations, synchronous code blocks the UI, making apps slow.This is where asyncio comes in. Need a Streamlit developer? Click
- [Streamlit Number Input](https://ryanandmattdatascience.com/streamlit-number-input/) - The st.number_input() widget in Streamlit allows users to input numeric values such as integers or floats.When should it be implemented? Acquiring numerical input from users. Obtaining budgets, quantities, or percentages. Price ranges are acceptable. Developing interfaces that are dynamic. Need a Streamlit Developer? Click Here Syntax st.number_input(label, min_value=None, max_value=None, value=”min”, step=None, format=None, key=None, help=None, on_change=None, args=None, kwargs=None, *, placeholder=None, disabled=False,
- [Streamlit Toggle](https://ryanandmattdatascience.com/streamlit-toggle/) - Users can enable or inhibit a binary option in your application using the Streamlit st.toggle() widget. It functions similarly to a switch button, yielding a boolean value (True or False). Need a Streamlit developer? Click Here Syntax st.toggle(label, value=False, key=None, help=None, on_change=None, args=None, kwargs=None, *, disabled=False, label_visibility=”visible”, width=”content”) ParameterTypeDefaultDescriptionlabelstrRequiredThe text label displayed next to the toggle switch. Used
- [Streamlit Calender](https://ryanandmattdatascience.com/streamlit-calender/) - Streamlit includes a built-in date picker meant for selecting dates (single or ranges).Why calendars are important in apps: for scheduling, planning events, filtering data, and more.A quick look at two different ways:Selection like a native calendar using st.date_input.The streamlit-calendar component gives you a full-featured calendar UI. Need a Streamlit developer? Click Here Native Date Input: st.date_input
- [Streamlit Divider](https://ryanandmattdatascience.com/streamlit-divider/) - In streamlit applications, content can get cluttered when displaying multiple sections,forms, widgets, or charts.In other to make the UI cleaner and easier to read, we use dividers.Dividers are horizontal lines that visually separate sectionsStreamlit provides a built-in function called st.divider() for this purpose. Need a Streamlit developer? Click Here Syntax st.divider(*, width="stretch") ParameterTypeDefaultDescriptionExamplewidth"stretch" or int"stretch"Defines the
- [Streamlit Logging](https://ryanandmattdatascience.com/streamlit-logging/) - Logging is a way to keep track of what happens inside your Streamlit app.It helps you:Fix bugs while you’re developing. Keep an eye on user events and interactions. Keep track of the phases in data processing. In Production, keep track of warnings and serious problems. Streamlit uses Python’s built-in logging module under the hood. Need a Streamlit developer? Click Here
- [Streamlit Fragment](https://ryanandmattdatascience.com/streamlit-fragment/) - When a widget changes, Streamlit usually runs your whole script again. Fragments break this pattern by letting you run parts of your app again, which makes it run faster and more responsively.The decorator @st.fragment() just runs the fragment, not the whole app.A fragment is a function decorated with @st.fragment that reruns independently from the whole
- [Streamlit Data Editior](https://ryanandmattdatascience.com/streamlit-data-editior/) - The data editor widget allows you to edit dataframes and many other data structures in a table-like UI.It’s like an Excel-like spreadsheet inside your Streamlit app. Need a Streamlit developer? Click Here Syntax st.data_editor(data, *, width="stretch", height="auto", use_container_width=None, hide_index=None, column_order=None, column_config=None, num_rows="fixed", disabled=False, key=None, on_change=None, args=None, kwargs=None, row_height=None) ParameterTypeDescriptiondataAny (DataFrame, Series, List, Dict, etc.)The data to
- [Streamlit Fastapi](https://ryanandmattdatascience.com/streamlit-fastapi/) - What is Streamlit?Python framework for creating interactive UIs.It is best for data dashboards, ML demos, and quick prototypes.What is FastAPIFastAPI is a modern, fast (high-performance), web framework for building APIs with Python based on standard Python type hints. Why Combine Streamlit and FastAPIExample Use Cases: Real-time dashboards AI-powered apps Multi-user booking systems Large-scale data APIs with beautiful UIs Need a Streamlit
- [How to Self Host N8N with Hostinger](https://ryanandmattdatascience.com/how-to-self-host-n8n-with-hostinger/) - Have you been paying $20 or more every month for N8N Cloud?What if I told you that you could self-host N8N for as little as $5/month and unlock more features, better privacy, and greater control over your workflows?In this comprehensive guide, I’ll walk you through everything you need to know to set up your own
- [Streamlit Expander](https://ryanandmattdatascience.com/streamlit-expander/) - The st.expander widget in Streamlit allows you to toggle visibility of a section of content. This is useful for organizing information and keeping the user interface clean by hiding less important details until the user chooses to view them.Users can click on the expander header to expand or collapse the content inside it. Need a Streamlit
- [Streamlit Popup](https://ryanandmattdatascience.com/streamlit-popup/) - we can write pop ups using st.dialogThe st.dialog feature in Streamlit allows you to create pop-up dialog boxes in your web application. These dialog boxes can be used to display information, gather user input, or confirm actionsA function decorated with @st.dialog becomes a dialog function. When you call a dialog function, Streamlit inserts a modal
- [Streamlit Plotly](https://ryanandmattdatascience.com/streamlit-plotly/) - Plotly is a charting library for Python, used for building interactive and animated data visualizations Streamlit supports Plotly through the st.plotly_chart() function.This function takes a Plotly figure object as input and renders it in the Streamlit app.You can create a variety of charts using Plotly, including line charts, bar charts, scatter plots, pie charts, and more.Plotly
- [Streamlit Line Chart](https://ryanandmattdatascience.com/streamlit-line-chart/) - A line chart visualization component for Streamlitst.line_chart() is used to create simple line charts from data.It is a high-level API that abstracts away the complexities of chart creation.Streamlit uses Altair under the hood for rendering line charts.The line chart is interactive, allowing users to hover over points to see values.It is particularly useful for visualizing
- [Streamlit Caching](https://ryanandmattdatascience.com/streamlit-caching/) - Streamlit runs your script from top to bottom whenever you interact with the app.This execution model makes development super easy. But it comes with two major challenges:1. Long-running functions run again and again, which slows down your app.2. Objects get recreated again and again, which makes it hard to persist them across reruns or sessions.Streamlit
- [Streamlit Session State](https://ryanandmattdatascience.com/streamlit-session-state/) - By default, Streamlit re-runs your script top to bottom whenever a user interacts with a widgetWhat this means is that all variables resets unless you use Session State Need a Streamlit developer?: Click Here Introduction st.session_state allows us toPreserve values across rerunsStore user inputs, counters or configurationsShare data between widgets and callbacksMange app state like in
- [Streamlit Form](https://ryanandmattdatascience.com/streamlit-form/) - Streamlit Form A form is a container that visually groups other elements and widgets together, and contains a Submit button. When the submit button is pressed, all widget values inside the form will be sent to Streamlit in a batch To add elements to a form object, we can use the “with” notation (more preferred),
- [Streamlit Checkbox](https://ryanandmattdatascience.com/streamlit-checkbox/) - st.checkbox display a checkbox widget.st.checkbox() widget in Streamlit is used to get boolean input from users.we can check True(checked) or False(unchecked). Need a Streamlit developer?: Click here Syntax st.checkbox(label, value=False, key=None, help=None, on_change=None, args=None, kwargs=None, disabled=False) ParametersParameter Type Descriptionlabel str Text displayed next to the checkboxvalue bool Default state (False = unchecked, True = checked)key str /
- [Streamlit Container](https://ryanandmattdatascience.com/streamlit-container/) - Inserts an invisible container into your app that can be used to hold multiple elements.This allows you to, for example, insert multiple elements into your app out of order. A container on Streamlit is a layout block that lets us group multiple elements together.It is useful when we want to have better control over the arrangement
- [Streamlit Pages](https://ryanandmattdatascience.com/streamlit-pages/) - Streamlit Pages allows us to create multi-pages apps for better navigation, structure and modularity.It helps us split our app into smaller pages.Some benefits of Streamlit Pages includesOrganized structure for large apps.Easy navigation through sidebar or custom menus.Reusable logic across pages.Better user experience.Setting up pagesStreamlit automatically recognizes a pages directory inside an app folder.Each Python file
- [Streamlit Components](https://ryanandmattdatascience.com/streamlit-components/) - Streamlit components are basically UI elements that allows users to interact with your app,and displays data visualizations, forms, charts and more. Need a Streamlit developer? Click here Streamlit Built-in Components ComponentDescriptionExamplest.titleDisplays a large titlest.title("My Streamlit App")st.headerDisplays a headerst.header("Section Header")st.subheaderDisplays a smaller headerst.subheader("Subsection")st.writeGeneral-purpose text displayst.write("This is Streamlit!")st.markdownRenders Markdown textst.markdown("**Bold Text**")st.codeDisplays code snippetsst.code("print('Hello')", language="python"). Lets write code to
- [Streamlit File Uploader](https://ryanandmattdatascience.com/streamlit-file-uploader/) - Streamlit File Uploaderst.file_uploader is a Streamlit widget used for uploading files directly from your local system.It supports single upload or multiple file uploads.By default st.file_uploader uploaded files are limited to 200MB each.We can configure this using the server.maxUploadSize config option. Need a Streamlit developer? Click here Syntax st.file_uploader(label, type=None, accept_multiple_files=False, key=None, help=None, on_change=None, args=None, kwargs=None, *,
- [Streamlit Write](https://ryanandmattdatascience.com/streamlit-write/) - This is the Swiss Army Knife of Streamlit commands.It does different things depending on what you throw at it. The st.write() function in Streamlit is a universal display function.It automatically detects the type of data we pass to it and renders it in the best possible format Need a Streamlit developer? Click Here Basic Syntax *args basically
- [Streamlit Map](https://ryanandmattdatascience.com/streamlit-map/) - st.map() is indeed used to visualize geospatial data on an interactive map.It plots points based on latitude and longitude coordinates.Streamlit internally uses PyDeck, which is built on Deck.gl (a WebGL-powered visualization framework).By default, Streamlit uses Mapbox tiles for rendering the background map Need a Streamlit Developer: Click Here Syntax: st.map(data=None, *, latitude=None, longitude=None, color=None, size=None, zoom=None,
- [n8n pdf generator](https://ryanandmattdatascience.com/n8n-pdf-generator/) - Companies often have to generate the same pdf or document files numerous times in a work week. I wanted to see if there was a shortcut to automate this process in n8n. My idea was to go from a central spreadsheet (Google Sheets or an Excel file), extract the info 1 row at a time,
- [Streamlit Emojis](https://ryanandmattdatascience.com/streamlit-emojis/) - Emojis have become a universal visual language.They are often utilized in applications and websites to:Design the UI to be inviting and captivating.Display status updates (✅, ❌, ⚠️)Deliver immediate visual feedbackImprove data dashboards 📊 Streamlit does not have a dedicated emoji widget; however, it permits the use of emojis in any text field. Headings and Titles Buttons and checkboxes Tables
- [Streamlit Text Area](https://ryanandmattdatascience.com/streamlit-text-area/) - st.text_area displays a multi-line text input widgetIt is used when we want users to enter large amounts of text such as feedback, descriptions , comments, e,t,cNeed a Streamlit developer? Click here Syntax st.text_area(label, value=””, height=None, max_chars=None, key=None, help=None, on_change=None, args=None, kwargs=None, *, placeholder=None, disabled=False, label_visibility=”visible”, width=”stretch”) ParameterTypeDefaultDescriptionlabelstrRequiredThe label or title displayed above the text area.valuestr""Default text
- [Streamlit Bar chart](https://ryanandmattdatascience.com/streamlit-bar-chart/) - One of Streamlit powerful features is the ability to visualize data using charts.we would focus on Bar chart in this article. Need a Streamlit developer? Click Here Basic Bar Chart Streamlit provides a simple way to display bar chart using the st.bar_chartwe also have other ways using Altair, Ploty and matplotlib which we would look at
- [Streamlit Radio Button](https://ryanandmattdatascience.com/streamlit-radio-button/) - A radio button is a Ui widget that let’s users select exactly one option from a list. Need a Streamlit developer? Click here To start we’re going to create a simple dataframe in python: Syntax location st.radio(label, options, index=0, format_func=special_internal_function, key=None, help=None, on_change=None, args=None, kwargs=None, *, disabled=False, horizontal=False, captions=None, label_visibility=”visible”, width=”content”) ParameterTypeDefaultDescriptionlabelstrRequiredThe label displayed above the radio
- [Streamlit Charts](https://ryanandmattdatascience.com/streamlit-charts/) - Streamlit allows us to view data(data visualization).It provides simple API’s for data visualization and supports both built-in charts and third party librariesStreamlit also have interactive charting libraries live Vega Lite(2D charts) and deck.gl(maps and 3D charts)and it provides chart types that are native to Streamlit, like st.line_chart and st.area_chartNeed a Streamlit? Click Here Built-in Streamlit
- [Streamlit Progress Bar](https://ryanandmattdatascience.com/streamlit-progress-bar/) - This displays a progress bar.In Streamlit, we use st.progress() to display a horizontal progress bar.st.progress(value, text=None) Need a Streamlit developer? Click here ParameterTypeDefaultDescriptionvalueint0Sets the initial progress (0–100).textstr (optional)NoneDisplays a label above the progress bar.width"stretch" or int"stretch"The width of the progress bar. • “stretch” → Expands to fit the parent container width. • int → A fixed width
- [Streamlit dropdown](https://ryanandmattdatascience.com/streamlit-dropdown/) - Dropdowns are compact ways to present multiple options, prevents invalid inputs and it’s great for filtering and navigation. we can have two types of dropdowns in streamlitst.selectbox: single selectionst.multiselect: multiple selections Need a Streamlit developer?: Click here Simple Selectbox import streamlit as st option = st.selectbox( "Choose an item", ["Bag", "Shoe", "Watch", "Cup"] ) st.write("you selected", option)
- [Streamlit Text Input](https://ryanandmattdatascience.com/streamlit-text-input/) - The st.text_input() widget is used to accept single line text input from users in Streamlit apps.It displays a single-line text input widgetIt is commonly used for:Usernames and passwordsSearch barsForm entriesDynamic filtering Are you looking for a streamlit developer? Click Here Parameters let’s quickly talk about some of the parameters in st.text_input() method st.text_input(label, value=””, max_chars=None, key=None,
- [Streamlit columns](https://ryanandmattdatascience.com/streamlit-columns/) - Columns allow us to display elements side by side horizontally .It is useful for dashboards, forms and various Ui layoutNeed a Streamlit developer?: Click here Introduction st.columns insert containers laid out as side by side columns.It inserts a number of multi-element containers laid out side by side and returns a list of container objects. Syntaxst.columns(spec, *,
- [Streamlit sidebar](https://ryanandmattdatascience.com/streamlit-sidebar/) - Streamlit sidebarSidebars are a great way to add navigation and settings to your Streamlit app.Elements can be passed to the sidebar. Need a Streamlit developer: Click Here Introduction A sidebar is a collapsible panel on the left side of the Streamlit app.It is used for navigation, filters and settings.The sidebar can be created using the st.sidebar
- [Streamlit Tabs](https://ryanandmattdatascience.com/streamlit-tabs/) - Tabs in Streamlit allow’s us to organize content into separate views inside the same appThey are useful for dashboards, multi-steps forms, reports, and when you want to avoid clutter. Need a Streamlit developer: Click here Some Basic usage of streamlit tab we use st.tabs to create tabs in streamlitwe can use the with notation to insert
- [Streamlit Table](https://ryanandmattdatascience.com/streamlit-table/) - Streamlit provides multiple ways to display tabular dataThese are:st.table() – Displays a static table.st.dataframe() – Displays an interactive table with sorting and filtering capabilities.st.write() – Can also be used to display tables, but is more versatile for various data types. st.table are the most basic way to display dataframes.Although it is generally recommended to use st.dataframe
- [Streamlit Chatbot](https://ryanandmattdatascience.com/streamlit-chatbot/) - Streamlit is a Python Framework for building interactive web apps with minimal code.In this article, we will demonstrate how to use Streamlit to create a simple chatbot interface. Need a streamlit developer: Click here Our First minimal chatbot interface using Streamlit This example will show how to create a basic chatbot interface using Streamlit.We will use
- [Streamlit Select Box](https://ryanandmattdatascience.com/streamlit-select-box/) - st.selectbox is a widget that allows users to select a single option from a dropdown list.It is useful for scenarios where you want to limit user input to a predefined set of options. Need a Streamlit developer: Click here st.selectbox(label, options, index=0, format_func=str, key=None, help=None)This is the basic syntax for creating a select box in Streamlit.label: The
- [Streamlit DataFrame](https://ryanandmattdatascience.com/streamlit-dataframe/) - Streamlit provides a powerful way to display and interact with data using DataFrames.DataFrames in Streamlit are typically used to display tabular data, allowing users to visualize and interact with datasets easily. Need a Streamlit developer: Click here 1. Introduction we would be using Pandas DataFrames to demonstrate how to use these features in Streamlit. import streamlit
- [Streamlit Button](https://ryanandmattdatascience.com/streamlit-button/) - Streamlit’s st.button() is the simplest way to create a button in your Streamlit app. It returns a boolean value indicating whether the button was clicked.It is the simplest way to add interactivitiyy to your app.It creates a clickable UI element that returns True only during the moment it’s clicked. Need a streamlit developer? Click here 1.
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- [Python If Elif Else](https://ryanandmattdatascience.com/python-if-elif-else/) - Master Python’s branching logic—if, elif, else—with clear examples, tips, and common pitfalls to avoid.
- [Python Variables](https://ryanandmattdatascience.com/python-variables/) - A beginner’s guide to Python variables—declaring, naming conventions, scoping rules, and real‑world examples
- [Python Convert Data Types](https://ryanandmattdatascience.com/python-convert-data-types/) - Learn how to safely convert between Python data types—ints, floats, strings, lists—with clear, practical code
- [Python Data Types](https://ryanandmattdatascience.com/python-data-types/) - Discover Python’s essential data types—strings, ints, floats, booleans & more—with examples and best practices
- [Python Operators](https://ryanandmattdatascience.com/python-operators/) - Get a full overview of Python operators—math, comparison, logical, bitwise—and how to use them in your code
- [Python Z-Score](https://ryanandmattdatascience.com/python-z-score/) - Learn how to compute and interpret Z‑scores in Python. Includes examples with NumPy & SciPy for data analysis
- [Python tuples](https://ryanandmattdatascience.com/python-tuples/) - Explore Python tuples—learn what they are, when to use them, and how to manipulate them in real‑world code
- [Python While Loops](https://ryanandmattdatascience.com/python-while-loops/) - Unlock the full power of Python while loops with clear examples, use cases, and performance best practices
- [eigenanalysis Python](https://ryanandmattdatascience.com/eigenanalysis-python/) - A clear, hands‑on guide to computing eigenvalues & eigenvectors in Python using NumPy—boost your linear algebra skills
- [Shapiro-Wilk Test Python](https://ryanandmattdatascience.com/shapiro-wilk-test-python/) - Check data normality in Python effortlessly—follow this guide to apply the Shapiro‑Wilk test and interpret results
- [mann-whitney u test python](https://ryanandmattdatascience.com/mann-whitney-u-test-python/) - Step into non‑parametric testing—learn how to perform the Mann‑Whitney U test in Python with real data and code
- [Spearman Rank Correlation](https://ryanandmattdatascience.com/spearman-rank-correlation-python/) - Discover how to compute and interpret Spearman’s rank correlation in Python—perfect for analyzing non‑parametric data
- [BeautifulSoup4 find vs find_all](https://ryanandmattdatascience.com/beautifulsoup-find-vs-find_all/) - Understand the difference between find and find_all in BeautifulSoup4. Learn when to use each with clear examples for efficient web scraping in Python.
- [web scraping with python](https://ryanandmattdatascience.com/web-scraping-with-python/) - Learn the basics behind scraping websites using Python. This covers robots.txt, rate limits, requests, and more
- [How to Normalize a Column Python Pandas](https://ryanandmattdatascience.com/normalize-column-python/) - Learn how to normalize a column in a pandas DataFrame using Python. This step-by-step guide covers Min-Max scaling, z-score standardization, and using sklearn.
- [Pandas Sample](https://ryanandmattdatascience.com/pandas-sample/) - Learn how to use pandas sample() to randomly select rows from a DataFrame. Includes examples for sampling with/without replacement and setting random state.
- [Pandas Where](https://ryanandmattdatascience.com/pandas-where/) - Discover how to use pandas where() to filter and modify DataFrame values. Learn syntax, real-world examples, and tips for cleaner, efficient Python code.
- [Law of Large Numbers Python](https://ryanandmattdatascience.com/law-of-large-numbers-python/) - See the Law of Large Numbers in action using Python simulations and visualization.
- [Python probability mass function](https://ryanandmattdatascience.com/python-probability-mass-function/) - Learn how to compute and use the Probability Mass Function (PMF) for discrete distributions in Python.
- [Python Probability Point Function](https://ryanandmattdatascience.com/python-probability-point-function/) - Understand the Probability Point Function (PPF) in Python for calculating inverse CDF values.
- [Binomial distribution python](https://ryanandmattdatascience.com/binomial-distribution-python/) - Learn to calculate and plot binomial distributions using Python’s SciPy and NumPy libraries.
- [Poisson distribution in python](https://ryanandmattdatascience.com/poisson-distribution-python/) - Model rare events with the Poisson distribution in Python. Step-by-step guide with examples.
- [uniform distribution python](https://ryanandmattdatascience.com/uniform-distribution-python/) - Generate and visualize uniform distributions using Python with NumPy and SciPy examples.
- [Harmonic Mean in Python](https://ryanandmattdatascience.com/harmonic-mean-python/) - Quickly calculate the harmonic mean using Python’s statistics and SciPy libraries. Simple code included.
- [Geometric Mean in Python](https://ryanandmattdatascience.com/geometric-mean-python/) - Learn how to compute the geometric mean using Python with NumPy, SciPy, and plain code examples.
- [python covariance matrix](https://ryanandmattdatascience.com/python-covariance-matrix/) - Calculate and visualize covariance matrices in Python using NumPy and Pandas. Real-world examples included.
- [Python Classes](https://ryanandmattdatascience.com/python-classes/) - Learn how to define, use, and master classes in Python with simple, practical code snippets.
- [time series seasonality python](https://ryanandmattdatascience.com/time-series-seasonality-python/) - Uncover hidden seasonal patterns in your time series using Python’s decomposition tools.
- [time series stationary python](https://ryanandmattdatascience.com/time-series-stationary-python/) - Learn how to test and transform time series data to stationary using Python tools.
- [machine learning imbalanced classes](https://ryanandmattdatascience.com/machine-learning-imbalanced-classes/) - Learn techniques to fix class imbalance in machine learning datasets for better model accuracy.
- [simpsons paradox in Python](https://ryanandmattdatascience.com/simpsons-paradox-in-python/) - See how Simpson’s Paradox can mislead your data analysis. Python examples reveal the truth.
- [Column Transformer](https://ryanandmattdatascience.com/column-transformer/) - Apply different preprocessing steps to columns using Column Transformer in sklearn. Clean and flexible!
- [Python Reduced Row Echelon Form](https://ryanandmattdatascience.com/python-reduced-row-echelon-form/) - Learn to find the RREF of matrices using SymPy or NumPy with hands-on examples
- [Lasso Regression](https://ryanandmattdatascience.com/lasso-regression/) - Apply Lasso Regression to shrink features and avoid overfitting using Python and scikit-learn.
- [Python Augmented Matrix](https://ryanandmattdatascience.com/python-augmented-matrix/) - Solve linear systems using augmented matrices in Python with NumPy step-by-step
- [scipy chi square test of independence](https://ryanandmattdatascience.com/scipy-chi-square-test-of-independence/) - Analyze categorical variables with the chi-square test of independence using Python’s SciPy.
- [python levenes test](https://ryanandmattdatascience.com/python-levenes-test/) - Use Levene’s test to check variance equality across groups with Python and SciPy
- [python one sample z test](https://ryanandmattdatascience.com/python-one-sample-z-test/) - Step-by-step guide to conducting a one-sample z-test in Python using stats and scipy.
- [Python One-Sample T-Test](https://ryanandmattdatascience.com/python-one-sample-t-test/) - Learn to perform a one-sample t-test using Python to compare sample means to known values.
- [Python Multiply Matrices](https://ryanandmattdatascience.com/python-multiply-matrices/) - Multiply matrices using NumPy and native Python. Step-by-step examples with explanations.
- [Inverse Matrix](https://ryanandmattdatascience.com/python-inverse-matrix/) - Calculate matrix inverses using NumPy and avoid common pitfalls in linear algebra operations.
- [Python Identity Matrix](https://ryanandmattdatascience.com/python-identity-matrix/) - Learn how to generate identity matrices using NumPy with simple, clear code examples
- [Ridge Regressor](https://ryanandmattdatascience.com/ridge-regressor/) - Improve your linear models with Ridge Regression. Learn how to apply it using scikit-learn.
- [Stacking Regressor](https://ryanandmattdatascience.com/stacking-regressor/) - Learn to boost model performance using stacking regression with multiple base learners.
- [Augmented Dickey–Fuller test](https://ryanandmattdatascience.com/augmented-dickey-fuller-test/) - Use the Augmented Dickey-Fuller (ADF) test to check for stationarity in time series data.
- [KPSS-test](https://ryanandmattdatascience.com/kpss-test/) - Check time series stationarity with the KPSS test. Step-by-step guide using Python
- [Multicollinearity](https://ryanandmattdatascience.com/multicollinearity/) - Understand multicollinearity in regression, how to detect it, and techniques to eliminate it for better models.
- [Simple Exponential Smoothing](https://ryanandmattdatascience.com/simple-exponential-smoothing/) - Master simple exponential smoothing for time series forecasting using Python and statsmodels.
- [Pandas Replace](https://ryanandmattdatascience.com/pandas-replace/) - Learn how to use pandas replace() to clean and update DataFrames. Replace values, strings, or NaNs with ease using practical Python examples and tips.
- [Python Determinant of a Matrix](https://ryanandmattdatascience.com/python-determinant/) - Learn how to calculate the determinant of a matrix in Python using NumPy. Step-by-step guide with examples to help you understand linear algebra basics fast.
- [Python F String](https://ryanandmattdatascience.com/python-f-string/) - Master Python f-strings for easy and efficient string formatting. Learn syntax, tips, and real examples to write cleaner, more readable Python code.
- [scipy chi square goodness of fit](https://ryanandmattdatascience.com/scipy-chi-square-goodness-of-fit/) - Learn how to perform a Chi-Square Goodness-of-Fit test using SciPy in Python. Step-by-step guide with examples to test categorical data distributions.
- [Python Regular Expressions](https://ryanandmattdatascience.com/regular-expressions/) - Learn Python regular expressions step by step. Discover how to search, match, and manipulate text using regex with practical examples and expert tips.
- [Pandas Interpolation](https://ryanandmattdatascience.com/pandas-interpolate/) - Learn how to fill missing data in pandas using the interpolate() method with examples, options, and best practices for data cleaning
- [PACF Partial Autocorrelation Function](https://ryanandmattdatascience.com/pacf-partial-autocorrelation-function/) - Learn what the partial autocorrelation function is, how it’s calculated, and why it's essential in time series analysis. Explore key formulas and examples.
- [Pandas Rank](https://ryanandmattdatascience.com/pandas-rank/) - Learn how to use rank() to rank data in your DataFrames. Discover ranking methods, tie-breaking strategies, and practical examples for data analysis.
- [Sklearn Support Vector Machine](https://ryanandmattdatascience.com/sklearn-support-vector-machine/) - Discover how Support Vector Machines work for classification problem in scikit learn with this tutorial and video
- [SKLearn Naive Bayes](https://ryanandmattdatascience.com/sklearn-naive-bayes/) - Learn the basics of Naive Bayes in scikit learn with this article and video. We cover a potential interview question and an example using the model
- [Pandas Expanding](https://ryanandmattdatascience.com/pandas-expanding/) - Explore how pandas.DataFrame.expanding() works to compute expanding window statistics. Get clear examples and tips for analyzing trends over time in your data.
- [LeetCode 182- Duplicate Emails (SQL & Python) Solutions](https://ryanandmattdatascience.com/leetcode-182/) - This article takes you step by step on how to solve LeetCode 182 Duplicate Emails in Both SQL as well as Python.
- [LeetCode 180- Consecutive Numbers (SQL & Python) Solutions](https://ryanandmattdatascience.com/leetcode-180/) - This article takes you step by step on how to solve LeetCode 180 Consecutive Numbers in Both SQL as well as Python.
- [LeetCode 181- Employees Earning More Than Their Managers (SQL & Python) Solutions](https://ryanandmattdatascience.com/leetcode-181/) - This article takes you step by step on how to solve LeetCode 181 Employees Earning More Than Their Managers in Both SQL as well as Python.
- [LeetCode 607 - Sales Person (SQL & Python) Solutions](https://ryanandmattdatascience.com/leetcode-607/) - This article takes you step by step on how to solve LeetCode 607 Sales Person in Both SQL as well as Python.
- [LeetCode 178- Rank Scores (SQL & Python) Solutions](https://ryanandmattdatascience.com/leetcode-178/) - This article takes you step by step on how to solve LeetCode 178 Rank Scores in Both SQL as well as Python.
- [LeetCode 184 - Department Highest Salary (SQL & Python) Solutions](https://ryanandmattdatascience.com/leetcode-184/) - This article takes you step by step on how to solve LeetCode 184 Department Highest Salary in Both SQL as well as Python.
- [LeetCode 176 - Second Highest Salary (SQL & Python) Solutions](https://ryanandmattdatascience.com/leetcode-176/) - This article takes you step by step on how to solve LeetCode 176 Second Highest Salary in Both SQL as well as Python.
- [LeetCode 175 - Combine Two Tables (SQL & Python) Solutions](https://ryanandmattdatascience.com/leetcode-175/) - This article takes you step by step on how to solve LeetCode 175 Combine Two Tables in Both SQL as well as Python.
- [SKlearn Multiple Linear Regressions](https://ryanandmattdatascience.com/sklearn-multiple-linear-regressions/) - Learn how to implement multiple linear regression using Scikit-Learn. Step-by-step guide with code examples for building and evaluating predictive models. 4o
- [Optuna Hyperparameter Tuning](https://ryanandmattdatascience.com/optuna-hyperparameter-tuning/) - Learn how to optimize machine learning models using Optuna for efficient and automated hyperparameter tuning saving you time and getting you better results
- [Python Enumerate](https://ryanandmattdatascience.com/python-enumerate/) - Discover how Python’s enumerate() adds indexes to loops. Learn syntax, use cases, and tips for cleaner, more efficient code.
- [Python Zip](https://ryanandmattdatascience.com/python-zip/) - Learn how Python’s zip() function works, with clear examples to combine, iterate, and unpack multiple iterables efficiently.
- [Python Dictionary Comprehension](https://ryanandmattdatascience.com/python-dictionary-comprehension/) - Learn how to use dictionary comprehension in Python with clear examples and tips for writing cleaner, more efficient code. 4o
- [Sklearn Gaussian Mixture Models](https://ryanandmattdatascience.com/sklearn-gaussian-mixture-models/) - Understand Gaussian Mixture Models (GMMs) in Python using scikit-learn. Learn how to model data distributions with practical, step-by-step examples.
- [Chat With a CSV Using LangChain](https://ryanandmattdatascience.com/chat-with-a-csv-using-langchain/) - Learn how to utilize agents to chat with a CSV file in around 10 minutes with Langchain and OpenAI
- [Python Match Case Statement](https://ryanandmattdatascience.com/python-match-case-statement/) - Learn how Python's match-case statements work with 6 clear examples. Master pattern matching for cleaner, more readable conditional logic.
- [Ordinal Encoder](https://ryanandmattdatascience.com/ordinal-encoder/) - Learn how to transform categorical features into ordinal values using scikit-learn's OrdinalEncoder. Includes clear, step-by-step Python examples.
- [One Hot Encoder](https://ryanandmattdatascience.com/one-hot-encoder/) - Learn how to apply OneHotEncoder in scikit-learn to convert categorical data into numerical format. Step-by-step guide with clear Python examples.
- [Train Test Split](https://ryanandmattdatascience.com/train-test-split/) - Train-test split is a key concept in machine learning. This article walks you through the entire process every data scientist should know
- [Python List Comprehension](https://ryanandmattdatascience.com/python-list-comprehension/) - Master Python list comprehensions with these 15 clear, practical examples. Learn how to write cleaner, more efficient code with this powerful one-liner technique.
- [Pandas Handle Missing Data](https://ryanandmattdatascience.com/pandas-handle-missing-data/) - Learn how to handle missing data in Python using Pandas with simple, effective techniques to clean and prepare your data for analysis.
- [Python Pandas GroupBy](https://ryanandmattdatascience.com/python-pandas-groupby/) - Learn how to use groupby in Python Pandas to summarize, split, and analyze your data with ease.
- [Box Cox Transformation Time Series](https://ryanandmattdatascience.com/box-cox-transformation-time-series/) - Struggling with unstable variance in your time series data? Use Box-Cox to transform your predictions from noisy to nice.
- [Python Validate User Input](https://ryanandmattdatascience.com/python-validate-user-input/) - Learn how to validate user input in Python with practical examples and tips. Ensure your programs run smoothly and securely by handling user input the right way
- [Pandas Query](https://ryanandmattdatascience.com/pandas-query/) - Tired of messy filters? Use pandas.query() to write cleaner, readable DataFrame queries. Fast, efficient, and easy to learn.
- [Python Pandas Explode](https://ryanandmattdatascience.com/pandas-explode/) - Learn how to use Pandas explode to transform list-like column values into separate rows with step-by-step examples and real-world use cases
- [Pandas Melt](https://ryanandmattdatascience.com/pandas-melt/) - Learn how to use Pandas melt to transform wide DataFrames into long format with clear, practical examples for real-world analysis
- [Pandas Merge](https://ryanandmattdatascience.com/pandas-merge/) - Learn how to merge DataFrames in Python Pandas with 7 examples covering inner, left, outer, and cross joins
- [Pandas Pivot](https://ryanandmattdatascience.com/pandas-pivot/) - "Pivots are extremely powerful tools in data analysis. This tutorial guides you from beginner to advanced-level pivot techniques in Pandas
- [Pandas iloc](https://ryanandmattdatascience.com/pandas-iloc/) - In Python Pandas, iloc lets you access rows and columns by integer position. This lesson walks through 12 practical examples
- [Scikit-learn Pipelines](https://ryanandmattdatascience.com/scikit-learn-pipelines/) - import pandas as pd import numpy as np import joblib from sklearn.model_selection import train_test_split from sklearn.impute import SimpleImputer from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.pipeline import make_pipeline, Pipeline from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.compose import ColumnTransformer d1 = {'Social_media_followers':[1000000, np.nan, 2000000, 1310000, 1700000, np.nan, 4100000, 1600000, 2200000, 1000000], 'Sold_out':[1,0,0,1,0,0,0,1,0,1]} df1 =
- [K-Nearest Neighbors](https://ryanandmattdatascience.com/k-nearest-neighbors/) - Comprehensive Understanding to K-Nearest Neighbors (KNN) in Supervised Machine Learning. K-Nearest Neighbors (KNN) is a simple, widely used supervised learning algorithm in data science and machine learning It was developed by Evelyn Fix and Joseph Hodges in 1951. Known for it usefulness and versatality, KNN can handle both classification and regression tasks when needed. The
- [Tree of Thought Prompting](https://ryanandmattdatascience.com/tree-of-thought-prompting/) - In the ever-evolving field of artificial intelligence, reasoning and problem-solving capabilities have seen remarkable advancements. One such innovation that stands out is the use of Tree of Thoughts (TOT) in AI reasoning, particularly in the realms of mathematical reasoning and writing TOT excels in guiding the progression of thoughts, making the problem-solving process more comprehensive
- [Chain of Thought Prompting](https://ryanandmattdatascience.com/chain-of-thought-prompting/) - Chain of Thought Prompting also known as COT is a way to enhance the reasoning and problem-solving abilities of Large Language Models (LLM). It helps guide the LLM through a step-by-step process to arrive at a final result. It’s like showing your work on a math problem. This is done by breaking down the prompt
- [Langchain Agents](https://ryanandmattdatascience.com/langchain-agents/) - Welcome to our latest article on Langchain agents! In this guide, we’ll dive into the innovative approach to building agents introduced in Langchain update 0.1. By leveraging agents, you can significantly enhance the capabilities of the OpenAI API and seamlessly integrate external tools. Interested in discussing a Data or AI project? Feel free to reach

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## Portfolio

- [Behind the feature](https://ryanandmattdatascience.com/portfolio/behind-the-feature/) - Provide insights into the development and decision-making process behind a specific product or service feature.
- [Running up that hill](https://ryanandmattdatascience.com/portfolio/running-up-that-hill/) - Convey the determination and effort required to overcome obstacles and reach one’s goals.
- [Visualizing concepts](https://ryanandmattdatascience.com/portfolio/visualizing-concepts/) - Discuss the power of graphical representations in clarifying complex ideas and information.
- [Full-scale expression](https://ryanandmattdatascience.com/portfolio/full-scale-expression/) - Investigate the significance of complete and unreserved self-expression in various creative endeavors.
- [Smooth handoff](https://ryanandmattdatascience.com/portfolio/smooth-handoff/) - Explore the seamless transfer of responsibilities between team members in a project or task.
- [Lingua franca](https://ryanandmattdatascience.com/portfolio/lingua-franca/) - Delves into the role of a common language in facilitating communication and understanding among cultures.
- [Just your type](https://ryanandmattdatascience.com/portfolio/just-your-type/) - Examine the importance of selecting the right font style and size for effective communication and design.
- [Throwing curveballs](https://ryanandmattdatascience.com/portfolio/throwing-curveballs/) - Delve into the strategy of introducing unexpected challenges or changes to test adaptability and problem-solving skills.
- [Illuminating dark mode](https://ryanandmattdatascience.com/portfolio/illuminating-dark-mode/) - Explore the benefits and design considerations of dark user interfaces in digital applications.

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## Categories

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