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Monitoring of Jobskills with Data Engineering & AI

Data Science Blog

However, we collect these over time and will make trends secure, for example how the demand for Python, SQL or specific tools such as dbt or Power BI changes. Over the time, it will provides you the answer on your questions related to which tool to learn! Why we did it? It is a nice show-case many people are interested in.

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

Data Science Dojo

Key Skills: Mastery in machine learning frameworks like PyTorch or TensorFlow is essential, along with a solid foundation in unsupervised learning methods. Stanford AI Lab recommends proficiency in deep learning, especially if working in experimental or cutting-edge areas. This role builds a foundation for specialization.

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Stay ahead of the curve with these 12 powerful GitHub repositories for learning data science, analytics, and engineering

Data Science Dojo

This blog lists down-trending data science, analytics, and engineering GitHub repositories that can help you with learning data science to build your own portfolio.  What is GitHub? GitHub is a powerful platform for data scientists, data analysts, data engineers, Python and R developers, and more.

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Future of Data and AI – March 2023 Edition 

Data Science Dojo

Data Storytelling in Action: This panel will discuss the importance of data visualization in storytelling in different industries, different visualization tools, tips on improving one’s visualization skills, personal experiences, breakthroughs, pressures, and frustrations as well as successes and failures.

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Data Scientist Job Description – What Companies Look For in 2025

Pickl AI

In the Indian context, data scientists often work in dynamic environments such as IT services, fintech, e-commerce, healthcare, and telecom sectors. They are expected to be versatile, handling everything from data engineering and exploratory analysis to deploying machine learning models and communicating insights to business stakeholders.

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The Rise and Fall of Data Science Trends: A 2018–2024 Conference Perspective

ODSC - Open Data Science

20212024: Interest declined as deep learning and pre-trained models took over, automating many tasks previously handled by classical ML techniques. While traditional machine learning remains fundamental, its dominance has waned in the face of deep learning and automated machine learning (AutoML).

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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. Data Visualization: Matplotlib, Seaborn, Tableau, etc.