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 ...

Course Objectives: To cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. To cover the basics needed for collecting, cleaning, and ...

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.

Find out what data cleaning is, its benefits and pieces, how it compares against data transformation and how to clean your data.

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

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

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.

Any process that involves the making of a prediction involves AI, and it is data scientists who create the algorithms that drive the underlying intelligence of these prediction processes.