Central Time CT
Data science has been called “the sexiest job of the 21st century” and with good reason—the size and breadth of our data is growing exponentially, making the ability to understand that data more and more challenging. This course provides a complete overview of the data science process and drills into detail on key tasks that occur before analytic model-building begins.
A project-oriented framework is used to introduce the discipline of data science, placing activities in the context of business value and covering key concepts every data scientist needs to know. Each project must define objectives, collect and integrate data, prepare it for analysis, perform the analysis, and deploy the results. Whether the end goal of the project is reporting, visualization, descriptive modeling, or predictive modeling, the same principles apply. For each stage, key principles are established and illustrated through real-world examples.
Next, the course breaks down the data science activities that occur before analytic modeling can begin. You may have heard that data scientists spend 80 percent of their time sourcing, cleaning, and preparing data. Although this may be an exaggeration (or not!)—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, it may not be easily accessible, or it may need to be transformed before we can start exploring the data and building models.
You will learn the principles and practices behind sourcing and preparing data for data science and predictive analytics projects. We will explore a motivating example from the speaker’s work and also touch on how analytics tools can be used in the data preparation workflow.
You Will Learn
- A solution-oriented project framework for successful data science projects
- How to translate business objectives into data science goals for maximum value
- Best practices for each project stage, the roles and resources required for success, and expected inputs and outputs
- Identification and evaluation of data sources to support analytics objectives
- Essential preparation activities that enable effective analytic modeling
- Analytics practitioners
- Data scientists
- Business analysts
- Data engineers and data pipeline developers
- Project leaders, technical managers, and engineers