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How to establish lineage transparency for your machine learning initiatives

IBM Journey to AI blog

But trust isn’t important only for executives; before executive trust can be established, data scientists and citizen data scientists who create and work with ML models must have faith in the data they’re using. This can lead to more accurate predictions and better decision-making.

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How to build reusable data cleaning pipelines with scikit-learn

Snorkel AI

Jason Goldfarb, senior data scientist at State Farm , gave a presentation entitled “Reusable Data Cleaning Pipelines in Python” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. It has always amazed me how much time the data cleaning portion of my job takes to complete.

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How to build reusable data cleaning pipelines with scikit-learn

Snorkel AI

Jason Goldfarb, senior data scientist at State Farm , gave a presentation entitled “Reusable Data Cleaning Pipelines in Python” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. It has always amazed me how much time the data cleaning portion of my job takes to complete.

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How to build reusable data cleaning pipelines with scikit-learn

Snorkel AI

Jason Goldfarb, senior data scientist at State Farm , gave a presentation entitled “Reusable Data Cleaning Pipelines in Python” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. It has always amazed me how much time the data cleaning portion of my job takes to complete.

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3 Takeaways from Gartner’s 2018 Data and Analytics Summit

DataRobot Blog

These modern tools will auto-profile the data, detect joins and overlaps, and offer recommendations. With AI infused throughout, the industry is moving towards a place where data analytics is far less biased, and where citizen data scientists will have greater power and agility to accomplish more in less time.

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The Modern Data Stack Explained: What The Future Holds

Alation

Powered by cloud computing, more data professionals have access to the data, too. Data analysts have access to the data warehouse using BI tools like Tableau; data scientists have access to data science tools, such as Dataiku. Better Data Culture. Data scientists. Data engineers.

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Data science

Dataconomy

Roles of data professionals Various professionals contribute to the data science ecosystem. Data scientists are the primary practitioners, employing methodologies to extract insights from complex datasets. Additionally, biases in algorithms can lead to skewed results, highlighting the need for careful data validation.