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Journeying into the realms of ML engineers and data scientists

Dataconomy

Machine learning engineer vs data scientist: two distinct roles with overlapping expertise, each essential in unlocking the power of data-driven insights. As businesses strive to stay competitive and make data-driven decisions, the roles of machine learning engineers and data scientists have gained prominence.

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How to Shift from Data Science to Data Engineering

ODSC - Open Data Science

Data engineering is a rapidly growing field, and there is a high demand for skilled data engineers. If you are a data scientist, you may be wondering if you can transition into data engineering. The good news is that there are many skills that data scientists already have that are transferable to data engineering.

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40 Must-Know Data Science Skills and Frameworks for 2023

ODSC - Open Data Science

The role of a data scientist is in demand and 2023 will be no exception. To get a better grip on those changes we reviewed over 25,000 data scientist job descriptions from that past year to find out what employers are looking for in 2023. Data Science Of course, a data scientist should know data science!

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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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How to become an AI Architect?

Pickl AI

AI Architects work closely with cross-functional teams, including data scientists, engineers, and business stakeholders, to design and deliver AI solutions that drive innovation, efficiency, and competitive advantage. Their responsibilities often revolve around coding, data preprocessing, model training, and optimization.

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Five benefits of a data catalog

IBM Journey to AI blog

Let’s look at five benefits of an enterprise data catalog and how they make Alex’s workflow more efficient and her data-driven analysis more informed and relevant. A data catalog replaces tedious request and data-wrangling processes with a fast and seamless user experience to manage and access data products.

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Using Snowflake Data as an Insurance Company

phData

A traditional approach requires massive efforts and a long lead time in sourcing from various data providers, data pipelining, and integrating into data marts. Also today’s volume, variety, and velocity of data, only intensify the data-sharing issues.