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How to Convert Jupyter Notebook into ML Web App?

Analytics Vidhya

Introduction Jupyter Notebook is a web-based interactive computing platform that many data scientists use for data wrangling, data visualization, and prototyping of their Machine Learning models. appeared first on Analytics Vidhya. The post How to Convert Jupyter Notebook into ML Web App?

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Navigate your way to success – Top 10 data science careers to pursue in 2023

Data Science Dojo

As the volume and complexity of data continue to surge, the demand for skilled professionals who can derive meaningful insights from this wealth of information has skyrocketed. Data Analyst Data analysts are responsible for collecting, analyzing, and interpreting large sets of data to identify patterns and trends.

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Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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How Dataiku and Snowflake Strengthen the Modern Data Stack

phData

The modern data stack is defined by its ability to handle large datasets, support complex analytical workflows, and scale effortlessly as data and business needs grow. Two key technologies that have become foundational for this type of architecture are the Snowflake AI Data Cloud and Dataiku.

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Top 5 Reasons You Should Become a Data Analyst

Smart Data Collective

As a data analyst, you will learn several technical skills that data analysts need to be successful, including: Programming skills. Data visualization capability. Data Mining skills. Data wrangling ability. Machine learning knowledge. Work in a Variety of Industries. Boost Problem-Solving Skills.

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State of Machine Learning Survey Results Part Two

ODSC - Open Data Science

Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation. What percentage of machine learning models developed in your organization get deployed to a production environment?

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Journeying into the realms of ML engineers and data scientists

Dataconomy

They employ statistical and mathematical techniques to uncover patterns, trends, and relationships within the data. Data scientists possess a deep understanding of statistical modeling, data visualization, and exploratory data analysis to derive actionable insights and drive business decisions.