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Remote Data Science Jobs: 5 High-Demand Roles for Career Growth

Data Science Dojo

For data scientists, this shift has opened up a global market of remote data science jobs, with top employers now prioritizing skills that allow remote professionals to thrive. Here’s everything you need to know to land a remote data science job, from advanced role insights to tips on making yourself an unbeatable candidate.

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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Rockets legacy data science environment challenges Rockets previous data science solution was built around Apache Spark and combined the use of a legacy version of the Hadoop environment and vendor-provided Data Science Experience development tools. Apache HBase was employed to offer real-time key-based access to data.

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Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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

Pickl AI

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Role of Data Scientists Data Scientists are the architects of data analysis.

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Data Warehouse vs. Data Lake

Precisely

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. Hadoop, Snowflake, Databricks and other products have rapidly gained adoption.

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Discover the Most Important Fundamentals of Data Engineering

Pickl AI

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

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Building Scalable AI Pipelines with MLOps: A Guide for Software Engineers

ODSC - Open Data Science

In today’s landscape, AI is becoming a major focus in developing and deploying machine learning models. It isn’t just about writing code or creating algorithms — it requires robust pipelines that handle data, model training, deployment, and maintenance. Model Training: Running computations to learn from the data.