Dataconomy

JULY 28, 2023

Missing data can lead to inaccurate results and biased analyses. Data scientists must decide on appropriate strategies to handle missing values, such as imputation with mean or median values or removing instances with missing data. What are the best data preprocessing tools of 2023?

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Snorkel AI

MAY 12, 2023

My name is Erin Babinski and I’m a data scientist at Capital One, and I’m speaking today with my colleagues Bayan and Kishore. We’re here to talk to you all about data-centric AI. To borrow another example from Andrew Ng, improving the quality of data can have a tremendous impact on model performance.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

MAY 12, 2023

My name is Erin Babinski and I’m a data scientist at Capital One, and I’m speaking today with my colleagues Bayan and Kishore. We’re here to talk to you all about data-centric AI. To borrow another example from Andrew Ng, improving the quality of data can have a tremendous impact on model performance.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

DagsHub

AUGUST 23, 2024

Data quality is crucial across various domains within an organization. For example, software engineers focus on operational accuracy and efficiency, while data scientists require clean data for training machine learning models. Without high-quality data, even the most advanced models can't deliver value.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52