But for simplicity lets discretize the tasks into the following 7 steps: In general the data science process is iterative and the different components blend together a little bit.
For this module we will use the definition:ĭata science is the process of formulating a quantitative question that can be answered with data, collecting and cleaning the data, analyzing the data, and communicating the answer to the question to a relevant audience. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.ĭata science has multiple definitions. In this specialization we assume familiarity with the R programming language. With this collection of tools at your disposal, as well as the techniques learned in the other courses in this specialization, you will be able to make key discoveries from your data for improving decision-making throughout your organization. Topics covered include hypothesis testing, linear regression, nonlinear modeling, and machine learning. This course covers the types of questions you can ask of data and the various modeling approaches that you can apply. Different modeling approaches can be chosen to detect interesting patterns in the data and identify hidden relationships.
3.5.7 TESTOUT LAB HOW TO
Building effective models requires understanding the different types of questions you can ask and how to map those questions to your data.