In this section, we use the open data SFMTA Bikeway Network at San Francisco Data. The data include the network of bike routes, lanes, and paths around the city of San Francisco. Maintained by the ...

Data cleaning, sometimes referred to as data munging or exploratory data analysis, explains the process of examining raw data and condensing it down to a more usable form.

Do data scientists really squander the bulk of their time cleaning data sets? Not necessarily - but for robust machine learning models, we do need better data management platforms.

But, as a new survey of data scientists and machine learners shows, those expectations need adjusting, because the biggest challenge in these professions is something quite mundane: cleaning dirty ...

Coursera offers a variety of training options for the growing data professional. Explore top data science courses from Coursera now.

Data quality is critical for successful AI projects, but you need to preserve the richness, variety, and integrity of the original data so you don’t sabotage the results.

Many enterprises, vendors, and startups often confuse the role of data scientist and data engineers. While the overlap of these roles is substantial they’re not particularly interchangeable.

AI-powered data cleaning tools use machine learning algorithms to automate data cleaning tasks such as data profiling, data matching, and data standardization.

A decade into the data revolution, the scramble for data science and analytic (DSA) talent is not slowing down. Companies in nearly every industry are re-tooling and gearing up their analytical ...