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Your Complete Roadmap to Become an Azure Data Scientist

Pickl AI

Summary: This blog provides a comprehensive roadmap for aspiring Azure Data Scientists, outlining the essential skills, certifications, and steps to build a successful career in Data Science using Microsoft Azure. What is Azure?

Azure 52
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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Big Data Processing: Apache Hadoop, Apache Spark, etc.

professionals

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Nurturing a Strong Data Science Foundation for Beginners

Mlearning.ai

This includes important stages such as feature engineering, model development, data pipeline construction, and data deployment. For example, when it comes to deploying projects on cloud platforms, different companies may utilize different providers like AWS, GCP, or Azure.

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Generative AI in Software Development

Mlearning.ai

There is a VSCode Extension that enables its integration into traditional development pipelines. How to use the Codex models to work with code - Azure OpenAI Service Codex is the model powering Github Copilot. GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API.

AI 52
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Data Scientists in the Age of AI Agents and AutoML

Towards AI

Simply put, focusing solely on data analysis, coding or modeling will no longer cuts it for most corporate jobs. I think a competitive data professional in 2025 must possess a comprehensive understanding of the entire data lifecycle without necessarily needing to be super good at coding per se.