---
title: "Data Science"
description: "1 posts about Data Science, summarised, each linking to the original."
---

# Data Science
> 1 posts about Data Science, summarised, each linking to the original.

## Articles

### [Data Science vs Data Engineering: Choosing Analysis or Infrastructure](https://yomu.fyi/post/data-science-vs-data-engineering-choosing-analysis-or-infrastructure.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: May 1, 2026

This guide distinguishes data engineering from data science by their roles in the data value chain. Data engineers build and maintain ingestion pipelines, warehouses, lakes, access controls, and production systems, using tools such as SQL, Python, Scala, Java, Apache Spark, Kafka, orchestration platforms, and cloud infrastructure. Data scientists work with clean, accessible data to perform exploratory analysis, engineer features, train and validate statistical or machine-learning models, and communicate findings through visualization and storytelling. The roles collaborate through feedback on data quality, shared documentation, model deployment infrastructure, and versioned schemas, feature pipelines, and experiments. For choosing a path, the guide recommends trying a two-week pipeline project and a two-week ML project; it also outlines portfolio deliverables for each and says both roles require strong analytical and computer-science fundamentals.