The Data Analytics Journey D204 (for WGU MSDA new path) with Complete Solutions

The Data Analytics Journey D204 with Complete SolutionsThe Data Analytics Journey D204 with Complete SolutionsThe Data Analytics Journey D204 with Complete Solutions Data preparation Time - ANSWER-data preparation 80%, and everything else falls into about 20% GIGO - ANSWER-garbage in, garbage out. That's a truism from computer science. The information you're going to get from your analysis is only as good as the information that you put into it Upside to In-house data - ANSWER-It's the fastest way to start., you may actually be able to talk with the people who gathered the data in the first place. Downside to In-house data - ANSWER-if it was an ad-hoc project, it may not be well documented. And the biggest one is the data simply may not exist. Maybe what you need really isn't there in your organization. Open data - ANSWER-Basically it's data that is free because it has no cost and it's free to use that you can integrate in your projects. Sources: Number one is government data, number two is scientific data and the third one is data from social media and tech companies APIs - ANSWER-An API or Application Programming Interface isn't a source of data but rather it's a way of sharing data, it can take data from one application to another. Uses JSON files Scraping data - ANSWER-Data scraping is, in a sense, the found art of data science. It's when you take the data that's around you, tables on pages and graphs in newspapers, and integrate that information into your data science work. Unlike the data that's available with API's or Application Programming Interfaces, which is specifically designed for sharing, Data scraping is for data that isn't necessarily created with that integration in mind.