You may have heard that data scientists spend 80 percent of their time sourcing, cleaning, and preparing data. Although this may or may not be an exaggeration, data preparation is certainly a large and important part of data science and predictive analytics. Data often does not start out in the ideal format; it may contain bad values, may not be easily accessible, or may need to be transformed before we can really start exploring it and building models.
In this session, we will provide an overview of sourcing and preparing data for data science and predictive analytics projects. We will use a motivating example from the speaker’s work and also touch on how Python, SQL, and Hadoop can be used in the data preparation workflow.
- Anyone getting started in data science who is interested in learning more about data preparation. This includes BI and analytics professionals and managers that are exploring the broader world of data science. Nontechnical professionals are welcome as well. Intermediate to advanced professional data scientists will find this session to be a review for them.