Top 7 n8n Workflow Templates for Data Science

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# Introduction

 
n8n is an open source workflow automation platform that allows you to connect applications, APIs, and services using a visual, node based interface. It helps automate data movement, system integrations, and repetitive tasks without requiring complex code. n8n is widely used because it is flexible, supports self hosting, integrates with hundreds of tools, and gives developers full control over logic, execution, and data handling, making it a strong alternative to closed automation platforms.

In this article, we will learn about the top 7 n8n workflow templates for data science. These templates are plug and play, meaning all you need to do is provide your data along with a model API or database API. Everything else is already tried and tested, allowing you to focus on analysis, experimentation, and results instead of building workflows from scratch.

# 1. Automate Fundamental Stock Analysis with FinnHub Data and Google Sheets (DCF Calculator)

Link to template: Automate Fundamental Stock Analysis with FinnHub Data and Google Sheets DCF Calculator | n8n workflow template

This n8n workflow automates the most time consuming parts of fundamental equity research by converting raw financial filings into institutional grade analysis at no execution cost. 

It pulls six years of annual and quarterly data from FinnHub, cleans and structures the financials, calculates accurate Trailing Twelve Months figures, computes three year and five year compound annual growth rates, and runs a full discounted cash flow valuation to estimate intrinsic stock value. 

All historical data, growth trends, and valuation results are automatically delivered to a connected Google Sheets dashboard with charts and tables that populate instantly for fast, objective analysis.

# 2. Automated Stock Technical Analysis with xAI Grok & Multi-channel Notifications

Link to template: Automated Stock Technical Analysis with xAI Grok & Multi-channel Notifications | n8n workflow template

This workflow is built for stock traders, financial analysts, portfolio managers, and investment enthusiasts who want automated, data driven stock market analysis without manual charting. 

It runs daily to analyze selected stocks using technical indicators such as relative strength index and moving average convergence divergence, generates clear buy, sell, or hold signals, and enhances the results with AI based interpretation and market news. 

The insights are automatically delivered through email, messaging apps, and a Google Sheets log, making it ideal for anyone who wants consistent trading signals, daily market summaries, and centralized tracking across multiple stocks.

# 3. Process OCR Documents from Google Drive into a Searchable Knowledge Base with OpenAI & Pinecone

Link to template: Process OCR Documents from Google Drive into Searchable Knowledge Base with OpenAI & Pinecone | n8n workflow template

This workflow automates a complete retrieval augmented generation ingestion pipeline for document indexing. When a new OCR JSON file is added to a Google Drive folder, it automatically extracts lesson metadata, cleans and parses the Arabic text, splits the content into semantic chunks, generates AI embeddings, and stores them in a Pinecone vector index for retrieval. 

Once processing is complete, the file is moved to an archive folder to prevent duplicate ingestion. Setup is simple and requires connecting Google Drive, OpenAI for embeddings, and Pinecone credentials, then configuring the input and archive folder paths before running the workflow.

# 4. Consolidate Data from 5 Sources for Automated Reporting with SQL, MongoDB & Google Tools

Link to template: Consolidate Data from 5 Sources for Automated Reporting with SQL, MongoDB & Google Tools | n8n workflow template

This workflow automatically consolidates data from Google Sheets, PostgreSQL, MongoDB, Microsoft SQL Server, and Google Analytics into a single master Google Sheet on a scheduled basis. 

Each dataset is tagged with a unique source identifier to maintain traceability, then merged, cleaned, and standardized into a consistent structure ready for reporting and analysis. 

The result is a centralized, always up to date reporting hub that eliminates manual data collection, reduces cleanup effort, and provides a reliable foundation for business insights across multiple systems.

# 5. Automate Data Extraction with Zyte AI (Products, Jobs, Articles & More)

Link to template: Automate Data Extraction with Zyte AI (Products, Jobs, Articles & More) | n8n workflow template

This workflow provides an automated AI powered web scraping solution that extracts structured data from e-commerce sites, articles, job boards, and search engine results without requiring custom selectors. 

Using the Zyte API, it automatically detects page structure, handles pagination, retries errors, and aggregates results through a two phase crawling and scraping process to produce a clean CSV export even for large websites. 

Users simply enter a target URL and select a scraping goal, while advanced logic routes the request to the correct extraction model. A manual mode is also available for users who prefer raw data output and custom parsing.

# 6. Customer Feedback Automation with Sentiment Analysis using GPT-4.1, Jira & Slack

Link to template: Customer Feedback Automation with Sentiment Analysis using GPT-4.1, Jira & Slack | n8n workflow template

This workflow automates the entire customer feedback lifecycle by collecting submissions through a webhook, validating the data, and using OpenAI to analyze sentiment. 

Negative feedback and feature requests are automatically converted into Jira issues, while invalid submissions trigger instant Slack alerts for quick action. In addition to real time processing, the workflow generates a weekly OpenAI powered summary of all feedback related Jira tickets and delivers it to Slack, giving teams a clear view of customer sentiment trends without manual reviews.

# 7. Real-Time Sales Pipeline Analytics with Bright Data, OpenAI, and Google Sheets

Link to template: Real-Time Sales Pipeline Analytics with Bright Data, OpenAI, and Google Sheets | n8n workflow template

This workflow automatically monitors key sales pipeline metrics such as new leads, deal stages, win rates, and stalled opportunities to keep teams informed about revenue health. 

It connects to your CRM on a schedule, analyzes pipeline data with OpenAI to detect risks and anomalies, sends actionable alerts and summaries to Slack, and stores daily snapshots in Google Sheets for trend analysis. The result is a fully automated sales visibility system that removes manual CRM exports and helps sales leaders, operations teams, and reps act faster and forecast more accurately.

# Final Thoughts

 
n8n has thousands of templates that can automate almost any data science workflow. The key is knowing which ones are genuinely useful, easy to plug in, and proven in real use. The seven templates listed above are some of the most practical options for data science because they cover the full pipeline, from data collection to analysis to delivery.

You can use them to automate financial analysis, generate technical trading insights, turn OCR documents into searchable knowledge bases, consolidate data from multiple databases for reporting, extract structured data from the web without building custom scrapers, analyze customer feedback with sentiment and issue tracking, and monitor sales pipelines in real time with alerts and dashboards.

If you want to move faster without constantly rebuilding the same tooling, these workflows are a strong starting point. Connect your data source, add your model or database credentials, and start iterating on the logic. You will spend less time on setup and more time on results.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.