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.

Data rarely comes in usable form. Data wrangling and exploratory data analysis are the difference between a good data science model and garbage in, garbage out.

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

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 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.

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 ...

Fractured and incomplete datasets are a key barrier towards effectively training AI models for deployment in healthcare settings.

It isn’t enough to simply capture your data. You must clean, process, analyze and visualize it to glean any insights. This is where data science tools and software make all the difference.

While traditional data science practices have paved the way for critical insights and informed decision-making, AI has propelled the discipline into a new era of unprecedented speed, scale and ...