Data Science Dojo

APRIL 29, 2025

Typically, datasets can have errors, missing values, or inconsistencies, so ensuring your data is clean and well-structured is essential for accurate analysis. Data profiling helps identify issues such as missing values, duplicates, or outliers.

Power BI 195

Power BI Analytics Analytics EDA 195

Pickl AI

JULY 24, 2024

Clear Formatting Remove any inconsistent formatting that may interfere with data processing, such as extra spaces or incomplete sentences. Validate Data Perform a final quality check to ensure the cleaned data meets the required standards and that the results from data processing appear logical and consistent.

Data Quality 52

Data Quality Machine Learning Machine Learning Clean Data 52

Dataconomy

JULY 28, 2023

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. The choice of approach depends on the impact of missing data on the overall dataset and the specific analysis or model being used.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

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

ODSC - Open Data Science

APRIL 15, 2025

LLMs, AI agents, and generative AI are the buzzwords lighting up the data science world. Because no modelno matter how powerfulcan perform well on poorly prepared data or without a solid development pipeline based on AIbasics. Data Wrangling: Taming the RawData Why it matters : Real-world data is messy.

Data Wrangling 52

Data Wrangling Data Science AI AI 52